applied scientist all genders b2b data science and algorithms

Zalando

<div><div>Ort</div><div>
Germany - Berlin</div><div>
Vertrag</div><div>
Full time</div><div>
Job-Kategorie</div><div>
Applied Science &amp; Research</div></div>
 <div><div><h1 class="jobSectionHeader">Applied Scientist (all genders) - B2B Data Science and Algorithms</h1>
The ZEOS department is responsible for all partner-facing Zalando Logistics Solutions. We provide a holistic approach to delivering the fulfillment solutions that meet our partner&rsquo;s needs by unifying these services under a single umbrella. We aim to provide our partners with a profitable fulfilment experience, and we see that to do this, Machine Learning, Operations Research and Data-driven solutions will play a pivotal role.
We are seeking an Applied Scientist who is fueled by the desire to build innovative and impactful ML/Optimization systems for our B2B logistics partners. You will join an existing team of Applied Scientists, and Machine Learning Engineers and work in a cross-functional setup with Product Managers, Data and Software Engineers.
The team&rsquo;s focus is helping our B2B partners improve inventory health and order fulfilment efficiency. You will be building various ML/DL forecasting models (demand, returns, lead-times), stochastic inventory optimization solutions, recommendation services and emerging Agentic AI systems that generate these key insights for our partners and enable them to make data-driven decisions about their article assortment and inventory. You will play a key role in a cross-functional team!<h2 class="jobSectionHeader">
WHY YOU SHOULD BE INTERESTED&hellip;</h2><ul><li>
Shape the Product from Day One: You won't be a &quot;model factory&quot; stuck in a silo! You will be a core scientific partner, working directly with product and user research to define the problem, not just solve it. Your expertise will directly influence the product roadmap and identify new opportunities to create value for our partners.</li></ul><ul><li>
Solve High-Stakes, Complex Scientific Problems: This is your chance to go beyond standard forecasting. You'll be tackling cutting-edge challenges in stochastic inventory optimization, multi-echelon demand forecasting (including returns and lead-times), and building recommender systems for assortment planning. Your work will be at the absolute core of our partners' profitability.</li></ul><ul><li>
Pioneer Agentic AI &amp; Robust Evaluations: As we explore opening up MCP servers to our partners, you will contribute to defining how we measure their performance/success in the user&rsquo;s agentic journey. You will design and implement rigorous Agentic AI evaluation frameworks/metrics (e.g., LLM-as-a-judge, trajectory evaluation, and safety guardrails) to ensure the autonomous systems connecting to the MCPs act reliably and logically on behalf of our B2B partners.</li></ul><ul><li>
End-to-End Ownership: You will have the autonomy to see your ideas through from initial research and prototyping all the way to production. You will define the metrics for success, collaborate with engineers to deploy your models as scalable services, and monitor their real-world impact on partner KPIs.</li></ul><ul><li>
Drive Impact at a multi-merchant scale: Your solutions won't just help one partner. You will be building platform-level services that scale across hundreds of diverse partners/merchants, directly influencing millions of euros in merchandise value and shaping the future of a more sustainable and efficient e-commerce logistics network.</li></ul> 
WE&rsquo;D LOVE TO MEET YOU IF&hellip;<ul><li>
Educational background in a quantitative field - Masters degree or higher preferred.</li><li>
3+ years of hands-on industry experience in an Applied Scientist, Data Scientist, or Research Scientist role, applying scientific methods to solve business problems.</li><li>
Industry demonstrated knowledge and skills in at least one the following areas:<ul><li>
Machine Learning or Deep Learning, particularly applied for time-series forecasting (e.g., LGBM, ARIMA, Prophet, Transformers, Nixtla, Darts,...)</li><li>
Machine Learning Engineering (e.g. service design, batch processing, GPU computing, git, docker, CI/CD, software testing)</li><li>
Operations Research and Optimization, (e.g., stochastic inventory models, linear/integer programming, Monte Carlo simulations, &hellip;)</li><li>
Agentic AI &amp; MCP evaluation frameworks</li></ul></li><li>
Proficiency in SQL and experience working with large-scale datasets.</li><li>
Strong communication skills with the ability to explain complex scientific concepts to product managers and business stakeholders. A collaborative, product-oriented mindset and a desire to see your work create real-world impact.</li><li>
An entrepreneurial mindset, you value a diverse and fast-paced business environment, and hence balance your passion for research with pragmatism to drive actions and create impact for our customers.</li><li>
You have a passion for learning and growing by doing. We are here to help you. One of the preferred values of the team is: &ldquo;High Challenge, High Support&rdquo;!</li><li>
Excellent communication skills! We cherish our open and direct feedback culture in the team!</li></ul>
INCLUSIVE BY DESIGN
At Zalando, our vision is to be the leading pan-European ecosystem for fashion and lifestyle e-commerce - one that is inclusive by design. We only assess candidates based on qualifications, merit, and business needs. We welcome applications from people of all gender identities, sexual orientations, personal expressions, racial identities, ethnicities, religious beliefs, and disability statuses. We only want to know why you're great for this role, so please avoid including your picture, age, and marital status in your CV as well.
We want to provide you with a great candidate experience. Please feel free to inform us of any accommodations you may need, so we can best support and assist you throughout the hiring process.
do.BETTER - our diversity &amp; inclusion strategy: https://jobs.zalando.com/en/our-culture/diversity-and-inclusion
OUR OFFER
Zalando provides a range of benefits, here's an overview of what you can expect. Ask your Talent Acquisition Partner to learn more about what we offer.<ul><li>
Employee shares program</li><li>
40% off fashion and beauty products sold and shipped by Zalando, 30% off Lounge by Zalando, discounts from external partners</li><li>
2 paid volunteering days per year</li><li>
Work from abroad for up to 30 working days a year</li><li>
27 days of vacation a year to start for full-time employees</li><li>
Family services, including counseling and support</li><li>
Health and wellbeing options (including Wellhub, formerly Gympass)</li><li>
Mental health support and coaching available</li><li>
Drive your development through our training platform and biannual peer-to-peer review</li></ul></div><div>
Recruiter
Casimir Udegbunam
casimir.udegbunam@zalando.de</div><div>
 Nach der Pr&uuml;fung werden unsere Recruiter*innen &uuml;ber eine offizielle Zalando E-Mail-Adresse (@zalando.de) Kontakt aufnehmen.
In einigen F&auml;llen arbeiten wir auch mit einer Auswahl von Headhunter*innen und Agenturen zusammen, um bestimmte Positionen zu besetzen. Bitte beachte, dass weder Zalando noch unsere Rekrutierungspartner*innen irgendeine Art von Bezahlung verlangen, um sich f&uuml;r eine Stelle zu bewerben oder an einem Vorstellungsgespr&auml;ch teilzunehmen.</div></div>
 <div></div><div><div><div><h2 class="jobSectionHeader">&Uuml;ber Zalando</h2>
Zalando wurde 2008 in Berlin gegr&uuml;ndet und ist die f&uuml;hrende europ&auml;ische Technologieplattform f&uuml;r Mode und Lifestyle. Wir verbinden 62 Millionen aktive Kund*innen mit mehr als 7.000 Marken in 29 M&auml;rkten. Unser Gesch&auml;ft basiert auf einer einzigartigen, KI-gest&uuml;tzten Daten- und Infrastrukturplattform. F&uuml;r unsere Kund*innen bietet unser Multi-App-Ansatz &ndash; bestehend aus Zalando, ABOUT YOU und Lounge by Zalando &ndash; ein inspirierendes, hochgradig personalisiertes Einkaufserlebnis. F&uuml;r unsere Partner bauen wir das Betriebssystem f&uuml;r den E-Commerce. &Uuml;ber ZEOS, Tradebyte und SCAYLE &ouml;ffnen wir unsere Logistik-, Software- und Servicekapazit&auml;ten f&uuml;r Marken und Einzelh&auml;ndler*innen zur nahtlosen, grenz&uuml;berschreitenden Skalierung ihres Gesch&auml;fts.</div></div></div>

Applied Scientist (all genders) - B2B Data Science and Algorithms

Amazon

<div>
 
 Description
 
 
 
 Are you interested in how to build AI reasoning systems that give provably correct answers? Are you excited by science at the interface of classical AI reasoning and Large Language Models (LLMs)? Would you like to apply your technology to serve operations customers better?
 
 
 Amazon Robotics is looking for a talented Applied Scientist. You will innovate on combining language models (LMs) with classical AI reasoning. You will work with a team of scientists and engineers to achieve this. You will publish your results in papers at leading venues in AI. You will be part of a larger team and have the opportunity to work on problems such as: using LMs to generate plans, using AI reasoning to verify plan correctness, learning efficient reasoning strategies, self-improving models. You will work on basic science and on business problems in robotics, automation and fulfillment across our operations.
 
 
 Key job responsibilities
 
 
 
 In This Role You Will
 
 
 
 <ul>
 <li>
 Work closely with other scientists and engineers, and be part of Amazon&rsquo;s diverse global science community.
 </li>
 <li>
 Publish your research in top-tier academic venues and hone your presentation skills.
 </li>
 <li>
 Be inspired by challenges and opportunities to invent new techniques in your area(s) of expertise.
 
 
 </li>
 </ul>
 A day in the life
 
 
 You'll meet regularly with your technical lead and your team on your ideas, get guidance and feedback, work together on architectures and algorithms, author papers, build AI systems, all with the aim of delivering results for your operations customers. You'll work closely with other scientists to review your plans and results. You'll meet with engineers to implement your ideas at scale.
 
 
 
 About The Team
 
 
 
 The Veritas team is a science team working at the boundary between language models and classical AI reasoning. We work across on customer problems in fulfillment, automation and robotics. We focus on high quality research science informed by practical problems.
 
 
 
 Basic Qualifications
 
 
 
 <ul>
 <li>
 Experience in building models for business application
 </li>
 <li>
 Experience in patents or publications at top-tier peer-reviewed conferences or journals
 </li>
 <li>
 Experience programming in Java, C++, Python or related language
 </li>
 <li>
 Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing
 </li>
 <li>
 Experience implementing algorithms using toolkits and self-developed code
 </li>
 <li>
 Publication record in top tier venues in generative AI reasoning or classical planning
 </li>
 <li>
 PhD in a relevant field (reinforcement learning, neurosymbolic AI, LLMs for formal reasoning)
 </li>
 <li>
 Experience in reinforcement learning or neuro-symbolic AI
 </li>
 <li>
 Practical experience with PyTorch, the HuggingFace ecosystem, SageMaker, and RL tools
 
 
 </li>
 </ul>
 
 Preferred Qualifications
 
 
 
 <ul>
 <li>
 Experience using Unix/Linux
 </li>
 <li>
 Experience in professional software development
 </li>
 <li>
 Experience using AWS tools and services - including AWS batch, Boto, S3, EC2 etc
 </li>
 <li>
 Strong skills in experimental design/statistical analysis
 </li>
 <li>
 Strong software engineering skills
 
 
 </li>
 </ul>
 Amazon is an equal opportunities employer. We believe passionately that employing a diverse workforce is central to our success. We make recruiting decisions based on your experience and skills. We value your passion to discover, invent, simplify and build. Protecting your privacy and the security of your data is a longstanding top priority for Amazon. Please consult our Privacy Notice (https://www.amazon.jobs/en/privacy_page) to know more about how we collect, use and transfer the personal data of our candidates.
 
 
 m/w/d
 
 
 Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you&rsquo;re applying in isn&rsquo;t listed, please contact your Recruiting Partner.
 
 
 
 
 Company
 
 - Amazon Development Center Germany GmbH
 
 
 Job ID: A10376861
</div>

Applied Scientist, Amazon Robotics

Norwegian Refugee Council

<div>What we are looking for:
We're looking for an experienced Data Scientist to help shape the future of humanitarian action through CLEAR (Crisis Learning, Early Warning and Anticipatory Action) - NRC's open infrastructure designed to transform fragmented crisis data into timely, actionable intelligence. 
CLEAR brings together diverse data sources - including earth observation, conflict data, field reports, and market intelligence - into a shared, open, and federated platform that enables humanitarian teams to anticipate crises and act before they escalate. By closing the gap between insight and action, CLEAR empowers faster, smarter, and more effective humanitarian responses. 
As the Data Scientist, you will play a pivotal role in designing and deploying cutting-edge machine learning and deep learning solutions that support early warning systems and humanitarian decision-making. You will develop predictive models capable of identifying emerging crisis patterns, forecasting humanitarian needs, and transforming complex, multimodal datasets into practical insights for field operations.
This is a highly technical, hands-on role suited to someone who enjoys building, training, and fine-tuning AI models from the ground up. You are comfortable working in data-scarce, rapidly evolving environments, proactively sourcing and preparing data, and solving complex challenges where real-world impact matters.
Working closely with the AI Lead, software developers, and data engineers, you will contribute across the full machine learning lifecycle - from model development and optimisation to performance monitoring and deployment. You will also help shape the technical environment in which models are trained and refined, ensuring AI solutions are scalable, robust, and aligned with the operational realities of humanitarian response. 
What you will do:
Here are some of your specific responsibilities
<ul><li>Support the design and implementation of CLEAR&rsquo;s data architecture.
</li><li>Partner with developers and data engineers to design and stand up the environment needed to train and fine-tune models (including data ingestion pipelines, compute and GPU resources, experiment tracking and MLOps tooling) actively shaping that environment rather than waiting for it to be provided.
</li><li>Develop machine learning and deep learning models that integrate multiple data streams to detect early indicators of humanitarian crises, combining earth observation and satellite imagery, conflict event data, climate monitoring, economic indicators, and population movement patterns into unified risk assessment frameworks.
</li><li>Build computer-vision and remote-sensing models on satellite and aerial imagery (for example flood-extent mapping, building and settlement detection, displacement-site monitoring, infrastructure damage assessment and land-cover change detection) and fuse these earth observation outputs with non-imagery signals.
</li><li>Contribute to building automated alert systems that identify emerging crises, calibrating prediction algorithms for different crisis types.
</li><li>Create ensemble modelling approaches that combine traditional statistical methods with advanced AI techniques.
</li><li>Fine-tune and adapt foundation models and large language models to humanitarian use cases such as document triage, multilingual report analysis and situation summarisation.
</li><li>Explore venues for adapting models to evolving crisis conditions through reinforcement learning systems.
</li><li>Implement impact-based forecasting systems that translate meteorological, conflict, and economic predictions into specific humanitarian consequences such as displacement volumes, food insecurity levels, and infrastructure damage estimates.
</li><li>Build decision trees and recommendation engines that guide field staff through systematic needs assessment processes informed by predictive analytics and historical response data.
</li><li>Create automated reporting systems and interactive dashboards that enable field teams and leadership to access real-time data for rapid response activities.
</li><li>Any other tasks assigned by the CLEAR Technical Lead and AI Lead related to CLEAR data.
</li></ul>Please download the detailed job description to learn more about this position.
What you will bring:
<ul><li>Advanced degree in Data Science, Statistics, Computer Science, Physics, Engineering, Economics or a related quantitative field, with a minimum of 5 years of professional experience in applied data science.
</li><li>Demonstrated experience designing, training and fine-tuning deep learning models (e.g. CNNs, recurrent/sequence models and transformers), including how to structure training runs, manage compute, and diagnose and improve model performance.
</li><li>Advanced proficiency in Python for statistical analysis, machine learning and data manipulation, with experience in key libraries including pandas, NumPy, scikit-learn, TensorFlow, PyTorch and Keras.
</li><li>Strong SQL skills for database management and complex query optimization.
</li><li>Hands-on experience implementing supervised and unsupervised learning algorithms including regression models, classification techniques, clustering methods, and time series analysis.
</li><li>A proven track record of proactively sourcing and engineering data (finding, negotiating access to, cleaning and, where necessary, generating data).
</li><li>Experience designing and implementing ETL pipelines for processing datasets from multiple sources, with skills in data cleaning, transformation and quality assurance at scale.
</li><li>Ability to create automated reporting systems and dashboards using tools like Tableau, Power BI or similar platforms.
</li><li>Experience working with large, messy, real-world datasets.
</li><li>Understanding of model deployment and MLOps practices, and comfort working with engineers to provision the infrastructure models need.
</li><li>Fundamental skills with version control software and collaborative development.
</li><li>Fluency in written and spoken English. Other languages are an asset.
</li><li>Practical experience working in low-data or data-scarce settings, including transfer learning, few-shot learning, data augmentation and synthetic data generation to overcome limited training data.
</li><li>Experience implementing and fine-tuning large language models (LLMs) for applied tasks.
</li><li>Experience with natural language processing techniques for analyzing reports, social media or news data relevant to crisis monitoring.
</li><li>Experience using GenAI for automated analysis of large volumes of documents &mdash; extracting key themes, sentiment analysis and identifying emerging trends across multiple contexts and languages.
</li><li>Understanding of ensemble methods and explainable AI techniques for transparent decision-making.
</li><li>Experience working with earth observation and satellite imagery; optical (e.g. Sentinel-2, Landsat) and radar / SAR (e.g. Sentinel-1), alongside commercial high-resolution sources, and with geospatial tooling such as Google Earth Engine, rasterio / GDAL, xarray and geopandas.
</li><li>Applying computer vision and deep learning to imagery (semantic segmentation, object detection, change detection) for humanitarian tasks such as flood-extent mapping, damage assessment, settlement and displacement-site detection, infrastructure monitoring and population estimation (a strong asset).
</li><li>Experience with multimodal data fusion.
</li></ul>What we offer:
<ul><li>Duty station: Remote (Germany, France, UK or Belgium)
</li><li>Contract: Fixed term (2 years)
</li><li>Travel: Up to 10%
</li><li>Salary/benefits: Grade 9 on NRC&rsquo;s Resident salary scale, with accompanying terms and conditions.
</li><li>NRC is an equal opportunities employer. We are committed to diversity without distinction to age, gender, religion, ethnicity, nationality, and physical ability.
</li><li>We think outside the box, encourage ideas, and give responsibility to all employees at all levels. You will have many opportunities to be heard and take the initiative.
</li></ul>Find out more about the benefits of working for NRC
Important information about the application process
<ul><li>For Internal candidates: To apply as an internal candidate, log in with your official email or click on Opportunity Market Place.
</li><li>When creating your profile, include your full name as given on your passport. Complete all the system-required fields for experience, employment history and education.
</li><li>Submit your application and CV in English, taking care to attach your latest CV.
</li><li>Applications that do not meet the minimum standards in terms of experience or qualifications will generally not be considered. Unsolicited applications not related to this specific job advertisement will not be considered.
</li><li>We receive many applications for each vacant position and so only shortlisted candidates will be contacted.
</li></ul>If you have any questions about this role, please email ho.recruitment@nrc.no with the job title as the subject line.
Why NRC?
The Norwegian Refugee Council (NRC) is an independent humanitarian organisation helping people forced to flee. Our 15,000 staff work in crises across 40 countries, providing life-saving and long-term assistance to millions of people every year.
Watch this short video to see NRC in action.
We are looking for people who are passionate about helping refugees and people forced to flee. Are you one of those people? If you are, NRC offers you the opportunity to:
<ul><li>do demanding and professional work, often in challenging contexts.
</li><li>join a work culture that empowers every employee to share ideas and take responsibility.
</li><li>be part of a welcoming and supportive community committed to human dignity.</li></ul></div>
 <div><div>The Norwegian Refugee Council (NRC) is a global humanitarian organisation helping people forced to flee. 
</div><div></div> 
<div>Join us in assisting millions of people in areas where others cannot, tackling some of the world's most dangerous and difficult crises. Bring your skills and dedication to an organisation recognised for providing high quality aid and for defending the rights of refugees and internally displaced people. 
</div><div></div> 
<div>At NRC, we give responsibility to employees at all levels and foster professional growth and innovative teams. You can expect a supportive culture and an open dialogue with management. We are committed to diversity, equity and inclusion. 
</div><div></div> 
<div>Together, we save lives and rebuild futures. 
</div><div></div> 
<div>Safeguarding is central to NRC&rsquo;s work. We expect all employees to:
</div><ul><li>treat everyone with respect and dignity
</li></ul><ul><li>contribute to building a safe environment for all
</li></ul><ul><li>never engage in any form of exploitation, harassment and specifically sexual exploitation, abuse and sexual harassment (SEAH)
</li></ul><ul><li>always report. NRC has a zero-tolerance approach to inaction against exploitation, abuse and SEAH</li></ul></div>

Data Scientist

Billie

<div>We are Billie, the leading provider of Buy Now, Pay Later (BNPL) payment methods for businesses, offering B2B companies innovative digital payment services and modern checkout solutions. We are to create a new standard for business payments and have made it our mission to simplify the purchasing experience for all businesses making it a tool for growth. Our solutions are based on proprietary, machine-learning-supported risk models, fully digitized processes and a highly scalable tech platform. This makes us a deep-tech company building financial products, not the other way around. We love building simple and elegant solutions and we strive for automation and scalability.
As part of Decision Science, you will work on the models that decide, in milliseconds, whether a transaction is trustworthy. Your work directly shapes how much fraud we stop, how many good customers we approve, and how much risk the business carries.<div></div> 
About the role:
As a Fraud Data Scientist, you will be a core technical contributor within Billie's Decision Science group. You will design and build robust, scalable machine learning solutions that prevent fraud, with a direct and measurable impact on Billie's bottom line. You will own the end-to-end modeling lifecycle: defining the analytical approach, testing hypotheses, and deploying models that capture complex debtor behavior and emerging fraud patterns.<div></div> 
In more detail, you will:<ul><li>
Design and ship anti-fraud models, taking ownership of project priorities and delivering production-ready solutions.</li><li>
Model debtor behavioral patterns, identify risk factors, and optimize the logic of Billie's real-time decision engine using quantitative analysis, data mining, and advanced ML.</li><li>
Balance precision and recall under severe class imbalance, explicitly weighing the cost of false positives (customer friction) against missed fraud (financial loss).</li><li>
Monitor deployed models for drift and adversarial adaptation, and retrain or recalibrate as fraud patterns shift.</li><li>
Collaborate with data and software engineers, analysts, and product managers to improve decision logic, integrate new data sources, and extend system functionality.</li><li>
Own the deployment and operationalization of ML services within real-time latency constraints, working with Engineering on infrastructure requirements such as containerization and event-driven architectures.</li><li>
Share knowledge across the team and contribute to strong experimentation and coding practices.</li><li>
Turn technical findings into clear, actionable recommendations through effective data storytelling for both technical and non-technical stakeholders.</li></ul>
What you bring to the team:<div></div> 
Must have:<ul><li>
3-5+ years in a quantitative or machine learning role, ideally in fintech or another high-transaction environment. Direct experience in fraud prevention or risk modeling is strongly preferred.</li><li>
Proven advanced proficiency in Python (e.g. pandas, scikit-learn, xgboost) and SQL (Snowflake, Postgres, or MySQL).</li><li>
Deep expertise in classification models (classical and deep learning), anomaly detection, and graph-based methods (e.g., graph neural networks, entity-link analysis).</li><li>
Hands-on experience productionizing ML services, with a strong grasp of modern MLOps concepts such as containerization (Docker/Kubernetes) and event-driven architectures.</li><li>
Proven ability to manage stakeholders across technical and non-technical functions, aligning technical roadmaps with business priorities.</li><li>
Sharp problem-solving skills, with the ability to translate complex business challenges into clean, efficient, and scalable technical requirements.</li><li>
Strong communication skills, with a track record of using data to influence strategy and drive cross-functional engagement.</li></ul>
Nice to have:<ul><li>
Experience with ML orchestration frameworks such as Metaflow, Apache Flink, or similar MLOps tooling.</li><li>
Experience implementing LLM-based workflows (e.g., agentic pipelines, retrieval-augmented generation, or LLM-assisted feature extraction), particularly applied to fraud detection or risk signals.</li></ul>
What We Offer:<ul><li>
Challenging and impactful work that drives personal and professional growth</li><li>
One of the best Virtual Shares Incentive Programs in the market, so that everyone at Billie is invested in our success</li><li>
Flexible work hours and trust in your ability to deliver, empowering you to take control of your work-life balance</li><li>
A hybrid working approach that allows you to work from home for up to 3 days per week</li><li>
Enjoy 30 days vacation per year, sabbatical opportunities, and extra child sickness leave for parents.</li><li>
Our &ldquo;Catch a Ride with Billie&rdquo; program that enables discounted access to Berlin Public Transport (BVG), Deutschland-Ticket, OR JobRad</li><li>
A yearly development budget to broaden your skill set and horizons</li><li>
Free German group classes</li><li>
An English-speaking, multicultural team with more than 40 nationalities</li><li>
Building meaningful connections with your colleagues through company and team events, interest groups, the Billie run club, game nights, and more, powered by our Formula Fun Team!</li></ul>
Billie is an equal opportunity employer and we do not discriminate on the basis of race, color, religion, sexual orientation, gender identity or expression, national origin, age, disability, or any other protected characteristic. We are committed to creating an inclusive environment where everyone feels they belong. All qualified applicants are welcome and we especially encourage you to apply, even if you don't check every box in the job description.
For information about data processing see our &quot;Recruiting Privacy Policy and further information&quot;: https://www.billie.io/en/candidate-information</div>

Fraud Data Scientist

ABOUT YOU SE & Co. KG

<div>Job Description 
We are looking for a Lead Backend Engineer (all genders) to be part of the development of our data-driven systems, specifically one of the Search, Recommendations and Sorting teams. These circles are embedded in our circle cluster &ldquo;Customer &amp; Shop Intelligence&rdquo;, which is in charge of driving a personalized and inspirational shop experience.
There are multiple possible areas you could work on. One is designing and implementing search algorithms, leveraging NLP techniques, and optimizing search results for relevance and efficiency to ensure our Search Engine delivers highly accurate and comprehensive results, enhancing the overall user experience. Another option would be working at the heart of our recommendation or ranking systems with deep insights into the algorithms behind that. Within this, you will work together with fellow engineers and data scientists to ensure that the systems serve customers in the best possible way, balancing speed and recommendation performance.<ul><li>
Develop smart BI solutions utilizing NodeJS with TypeScript</li><li>
Manage extensive data volumes efficiently, employing Google BigQuery and integrating seamlessly with Business Intelligence systems</li><li>
Build and enhance high-traffic APIs, ensuring minimal latencies</li><li>
Collaborate closely with seasoned developers, product managers, data scientists and analysts to enhance current functionalities and launch novel features</li><li>
Encouragement for innovative ideas and creations, a flat organizational structure, and rapid decision-making processes ensure ample room for contribution and growth</li><li>
Draft architectural decisions together with our Tech Lead and explore new technologies</li></ul> 
Qualifications 
<ul><li>
5+ years experience in building scalable APIs (e.g. using node.js, Python or Go)</li><li>
Practical &amp; recent experience with a typed programming language (e.g. TypeScript)</li><li>
Solid know how in working with SQL or No-SQL databases</li><li>
You write well-structured, efficient and maintainable code and actively keep the quality of the code base in check</li><li>
You have excellent analytical and problem-solving skills</li><li>
You are used to working in an English-speaking and agile environment</li><li>
You raise the bar of the software you work on</li><li>
You enjoy being responsible for a product, owning it deeply and as well push for own improvement ideas to work on within the team</li><li>
You establish standards within your profession, as well during collaboration with fellow engineers across team boundaries</li></ul>
Nice To Have<ul><li>
Experience in working with a MPP data warehouse (e.g. Redshift, BigQuery)</li><li>
Experience with centralized logging, monitoring, and alerting solutions (Datadog)</li><li>
Hands-on experience with a cloud ecosystem (e.g. AWS, GCP) and integration with orchestration services (like Dagster or Airflow)</li><li>
Experience with Elasticsearch or OpenSearch</li><li>
Experience building Data Models using dbt</li><li>
Experience of running data processing pipelines using distributed data processing frameworks</li><li>
Hands-on Experience with Machine Learning Models or LLM-based systems is a plus</li></ul> 

Additional Information<div></div> 
Your perks at a glance: Visit our benefits page. 
 
Simply apply online via our career page - we will get back to you as soon as possible! 
 
A Place Where You Can Be You 
We take it as our responsibility to create an environment where everyone feels welcome, exactly as they are. 
Different backgrounds and perspectives make us stronger and shape our culture in ways that matter. 
What we stand for internally, we stand for as a brand: acceptance, inclusion, and a fairer approach to fashion.</div>

Lead Backend Engineer (all genders)

Zeta Global

<div>WHO WE ARE

Zeta Global (NYSE: ZETA) is the AI-Powered Marketing Cloud that leverages advanced artificial intelligence (AI) and trillions of consumer signals to make it easier for marketers to acquire, grow, and retain customers more efficiently. Through the Zeta Marketing Platform (ZMP), our vision is to make sophisticated marketing simple by unifying identity, intelligence, and omnichannel activation into a single platform &ndash; powered by one of the industry&rsquo;s largest proprietary databases and AI. Our enterprise customers across multiple verticals are empowered to personalize experiences with consumers at an individual level across every channel, delivering better results for marketing programs. Zeta was founded in 2007 by David A. Steinberg and John Sculley and is headquartered in New York City with offices around the world. To learn more, go to www.zetaglobal.com. 


The Role

We&rsquo;re looking for a skilled ML Engineer / Data Scientist with 3+ years of software or applied ML experience to design, build, and improve machine learning solutions in a dynamic cloud environment, primarily on AWS.This role sits at the intersection of data science and engineering: exploring data, developing models, running rigorous experiments, and bringing the best approaches into production with a reliable, reproducible workflow. If strong Python skills, curiosity about hard modeling problems, and collaborative work in multicultural teams are a fit, this is a chance to do meaningful, end-to-end ML work&mdash;not just notebooks, and not just infrastructure. 

<div>
Who you are:
<ul>
<li>Strong foundation in machine learning, statistics and experiment design.</li>
<li>Experience building models for real business or product problems, not only academic benchmarks.</li>
<li>Comfortable working with structured and unstructured data: feature engineering, dataset construction, labeling quality, leakage checks, and train/validation/test discipline.</li>
<li>Able to compare approaches with clear metrics, error analysis, and sound judgment about tradeoffs (accuracy, latency, cost, maintainability).</li>
<li>Interest in modern ML, including classical ML, deep learning, and LLM / GenAI workflows where relevant (fine-tuning, RAG, evaluation, prompt/versioning).</li>
<li>Proficient in Python and able to write clean, modular, testable code.</li>
<li>Experience developing and deploying ML solutions in a cloud environment, especially AWS.</li>
<li>Comfortable moving from prototype to production: packaging models, building inference paths, monitoring performance, and iterating after launch.</li>
<li>Independent engineer who can own work from problem framing experimentation implementation rollout.</li>
<li>Excellent written and spoken English.</li>
<li>Enjoy working closely with engineers, product partners, and other data scientists.</li>
<li>Clear communicator who can explain methods, results, and limitations to technical and non-technical audiences.</li>
<li>Master&rsquo;s degree in Science or Engineering (Computer Science, Mathematics, Physics, Statistics, or similar), or equivalent practical experience.</li>
</ul>
Nice to have:
<ul>
<li>Experience with scikit-learn, PyTorch, TensorFlow, XGBoost, or similar modeling stacks.</li>
<li>Familiarity with ML experiment tracking and reproducibility (e.g. MLflow, W&amp;B).</li>
<li>Experience with SQL, data warehouses/lakes, and pipeline tools such as Airflow, dbt, or Spark.</li>
<li>Exposure to feature stores, embedding pipelines, or vector search for retrieval-based systems.</li>
<li>Experience building HTTP/gRPC APIs or lightweight services around model inference.</li>
<li>Working knowledge of Docker, basic orchestration, and CI/CD (e.g. GitLab CI).</li>
<li>Experience in agile, remote and async team environments.</li>
<li>Publications, patents, Kaggle/competition results, or open-source ML contributions.</li>
</ul>
What you might like about this role:
<ul>
<li>Hands-on modeling work with room to explore, benchmark, and improve real systems.</li>
<li>Collaboration on ML patent submissions and participation in weekly ML / research paper review meetings.</li>
<li>A multicultural, engineering-focused team with strong peer support.</li>
<li>High trust and autonomy&mdash;clear goals, freedom in how to reach them.</li>
<li>Internal product impact: meaningful projects that improve developer and user experience, not endless maintenance tickets.</li>
<li>Short approval cycles and solid product partnership.</li>
<li>A healthy meeting policy and emphasis on protecting focus time.</li>
<li>Flexible hours, remote/home office options, and a calm, engineers-only office when on-site.</li>
<li>Competitive compensation, including stock options.</li></ul> 
<div>
</div><div></div><div><div>We&rsquo;re hiring across multiple levels. Title, scope, and compensation depend on experience&mdash;from strong applied ML generalists to senior people who can lead modeling direction and mentor others.

We&rsquo;re especially interested in candidates who are technically strong, intellectually curious, and motivated by difficult, ambiguous problems where good data science and solid engineering both matter. 


PEOPLE &amp; CULTURE AT ZETA

Zeta considers applicants for employment without regard to, and does not discriminate on the basis of an individual&rsquo;s sex, race, color, religion, age, disability, status as a veteran, or national or ethnic origin; nor does Zeta discriminate on the basis of sexual orientation, gender identity or expression.
We&rsquo;re committed to building a workplace culture of trust and belonging, so everyone feels invited to bring their whole selves to work. We provide a forum for employees to celebrate, support and advocate for one another. Learn more about our commitment to diversity, equity and inclusion here: https://zetaglobal.com/blog/a-look-into-zetas-ergs/

ZETA IN THE NEWS!

https://zetaglobal.com/press/?cat=press-releases 


#LI-NP1</div></div></div></div>

ML Ops Engineer

<div>
 
 &nbsp;
 
 
 
 WHO WE ARE&nbsp;
 
 
 
 Zeta Global (NYSE: ZETA) is the AI-Powered Marketing Cloud that leverages advanced artificial intelligence (AI) and trillions of consumer signals to make it easier for marketers to acquire, grow, and retain customers more efficiently. Through the Zeta Marketing Platform (ZMP), our vision is to make sophisticated marketing simple by unifying identity, intelligence, and omnichannel activation into a single platform &ndash; powered by one of the industry&rsquo;s largest proprietary databases and AI. Our enterprise customers across multiple verticals are empowered to personalize experiences with consumers at an individual level across every channel, delivering better results for marketing programs. Zeta was founded in 2007 by David A. Steinberg and John Sculley and is headquartered in New York City with offices around the world. To learn more, go to www.zetaglobal.com.
 
 
 &nbsp;
 
 
 
 The Role
 
 
 
 We&rsquo;re looking for a skilled&nbsp;
 
 ML Engineer / Data Scientist
 
 &nbsp;with&nbsp;
 
 3+ years of software or applied ML experience
 
 &nbsp;to design, build, and improve machine learning solutions in a dynamic cloud environment, primarily on&nbsp;
 
 AWS
 
 .This role sits at the intersection of&nbsp;
 
 data science and engineering
 
 : exploring data, developing models, running rigorous experiments, and bringing the best approaches into production with a reliable, reproducible workflow. If strong Python skills, curiosity about hard modeling problems, and collaborative work in multicultural teams are a fit, this is a chance to do meaningful, end-to-end ML work&mdash;not just notebooks, and not just infrastructure.
 
 
 &nbsp;
 
 
 
 Who you are:
 
 
 <ul>
 <li>
 Strong foundation in&nbsp;
 
 machine learning,&nbsp;statistics
 
 and&nbsp;
 
 experiment design
 
 .
 </li>
 <li>
 Experience building models for&nbsp;
 
 real business or product problems
 
 , not only academic benchmarks.
 </li>
 <li>
 Comfortable working with&nbsp;
 
 structured and unstructured data
 
 : feature engineering, dataset construction, labeling quality, leakage checks, and train/validation/test discipline.
 </li>
 <li>
 Able to compare approaches with clear&nbsp;
 
 metrics
 
 , error analysis, and sound judgment about tradeoffs (accuracy, latency, cost, maintainability).
 </li>
 <li>
 Interest in&nbsp;
 
 modern ML
 
 , including classical ML, deep learning, and&nbsp;
 
 LLM / GenAI workflows
 
 &nbsp;where relevant (fine-tuning, RAG, evaluation, prompt/versioning).
 </li>
 <li>
 Proficient in&nbsp;
 
 Python
 
 &nbsp;and able to write&nbsp;
 
 clean, modular, testable&nbsp;code
 
 .
 </li>
 <li>
 Experience developing and deploying ML solutions in a&nbsp;
 
 cloud environment
 
 , especially&nbsp;AWS.
 </li>
 <li>
 Comfortable moving from prototype to production: packaging models, building inference paths, monitoring performance, and iterating after launch.
 </li>
 <li>
 Independent engineer who can own work from&nbsp;
 
 problem framing &rarr; experimentation &rarr; implementation &rarr; rollout
 
 .
 </li>
 <li>
 Excellent&nbsp;
 
 written and spoken English
 
 .
 </li>
 <li>
 Enjoy working closely with engineers, product partners, and other data scientists.
 </li>
 <li>
 Clear communicator who can explain methods, results, and limitations to technical and non-technical audiences.
 </li>
 <li>
 
 Master&rsquo;s degree
 
 &nbsp;in Science or Engineering (Computer Science, Mathematics, Physics, Statistics, or similar),&nbsp;
 
 or equivalent practical experience
 
 .
 </li>
 </ul>
 
 
 Nice to have:
 
 
 <ul>
 <li>
 Experience with&nbsp;
 
 scikit-learn,&nbsp;PyTorch,&nbsp;TensorFlow,&nbsp;XGBoost
 
 , or similar modeling stacks.
 </li>
 <li>
 Familiarity with&nbsp;
 
 ML experiment tracking
 
 &nbsp;and reproducibility (e.g. MLflow, W&amp;B).
 </li>
 <li>
 Experience with&nbsp;
 
 SQL
 
 , data warehouses/lakes, and pipeline tools such as&nbsp;
 
 Airflow,&nbsp;dbt, or&nbsp;Spark
 
 .
 </li>
 <li>
 Exposure to&nbsp;
 
 feature stores
 
 , embedding pipelines, or&nbsp;
 
 vector search
 
 &nbsp;for retrieval-based systems.
 </li>
 <li>
 Experience building&nbsp;
 
 HTTP/gRPC APIs
 
 &nbsp;or lightweight services around model inference.
 </li>
 <li>
 Working knowledge of&nbsp;
 
 Docker
 
 , basic orchestration, and CI/CD (e.g.&nbsp;
 
 GitLab CI
 
 ).
 </li>
 <li>
 Experience in&nbsp;
 
 agile
 
 ,&nbsp;
 
 remote
 
 and&nbsp;
 
 async
 
 &nbsp;team environments.
 </li>
 <li>
 Publications, patents, Kaggle/competition results, or open-source ML contributions.
 </li>
 </ul>
 
 
 What you might like about this role:
 
 
 <ul>
 <li>
 
 Hands-on modeling work
 
 &nbsp;with room to explore, benchmark, and improve real systems.
 </li>
 <li>
 Collaboration on&nbsp;
 
 ML patent submissions
 
 &nbsp;and participation in&nbsp;weekly ML / research paper review&nbsp;meetings.
 </li>
 <li>
 A&nbsp;
 
 multicultural, engineering-focused team
 
 &nbsp;with strong peer support.
 </li>
 <li>
 
 High trust and autonomy
 
 &mdash;clear goals, freedom in how to reach them.
 </li>
 <li>
 
 Internal product impact
 
 : meaningful projects that improve developer and user experience, not endless maintenance tickets.
 </li>
 <li>
 
 Short approval cycles
 
 &nbsp;and solid product partnership.
 </li>
 <li>
 A&nbsp;
 
 healthy meeting policy&nbsp;
 
 and emphasis on protecting focus time.
 </li>
 <li>
 
 Flexible hours
 
 , remote/home office options, and a calm, engineers-only office when on-site.
 </li>
 <li>
 
 Competitive compensation
 
 , including&nbsp;stock options.
 </li>
 </ul>
 &nbsp;
 
 We&rsquo;re hiring across
 
 multiple levels
 
 . Title, scope, and compensation depend on experience&mdash;from strong applied ML generalists to senior people who can lead modeling direction and mentor others.
 
 
 We&rsquo;re especially interested in candidates who are&nbsp;
 
 technically strong, intellectually curious, and motivated by difficult, ambiguous problems
 
 &nbsp;where good data science and solid engineering both matter.
 
 
 &nbsp;
 
 
 &nbsp;
 
 
 
 PEOPLE &amp; CULTURE AT ZETA
 
 
 
 Zeta considers applicants for employment without regard to, and does not discriminate on the basis of an individual&rsquo;s sex, race, color, religion, age, disability, status as a veteran, or national or ethnic origin; nor does Zeta discriminate on the basis of sexual orientation, gender identity or expression.&nbsp;&nbsp;
 
 
 We&rsquo;re committed to building a workplace culture of trust and belonging, so everyone feels invited to bring their whole selves to work. We provide a forum for employees to celebrate, support and advocate for one another. Learn more about our commitment to diversity, equity and inclusion here:&nbsp; https://zetaglobal.com/blog/a-look-into-zetas-ergs/&nbsp;
 
 
 
 ZETA IN THE NEWS!
 
 
 
 https://zetaglobal.com/press/?cat=press-releases&nbsp;
 
 
 &nbsp;
 
 
 
 
 
</div>

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