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advisor - scientific data engineer Jobs in San Francisco

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ai data engineer (agent platform) Jobs in San Francisco

ai data engineer (agent platform)

ai engineer, ai & data platforms (aidp) Jobs in San Francisco

ai engineer, ai & data platforms (aidp)

senior data scientist

Amazon.com

<div><h2 class="jobSectionHeader">DESCRIPTION</h2><div>
AWS Infrastructure Services owns the design, planning, delivery, and operation of all AWS global infrastructure. In other words, we&rsquo;re the people who keep the cloud running. We support all AWS data centers and all of the servers, storage, networking, power, and cooling equipment that ensure our customers have continual access to the innovation they rely on. We work on the most challenging problems, with thousands of variables impacting the supply chain &mdash; and we&rsquo;re looking for talented people who want to help. 
 
You&rsquo;ll join a diverse team of software, hardware, and network engineers, supply chain specialists, security experts, operations managers, and other vital roles. You&rsquo;ll collaborate with people across AWS to help us deliver the highest standards for safety and security while providing seemingly infinite capacity at the lowest possible cost for our customers. And you&rsquo;ll experience an inclusive culture that welcomes bold ideas and empowers you to own them to completion.
 
 
The Data Center Field Engineering Team is the engineering owner for the lifecycle of AWS data center mechanical and electrical infrastructure. This includes supporting new designs and innovations through data center end-of-life, with a focus on root cause analysis of failures, capacity and availability improvement, and optimization of the existing fleet. 
 
As a Senior Data Scientist on the Field Engineering Portfolio team, you will bring advanced analytical and machine learning capabilities to one of the most critical infrastructure organizations at AWS. You will develop scalable models and data-driven frameworks that measure, predict, and improve fleet performance &mdash; including data center availability, operational efficiency, and key performance indicators (KPIs) across the global AWS data center fleet. 
 
You are an exceptionally strong communicator, both written and verbally, capable of translating complex quantitative findings into clear recommendations for senior engineering and business leadership. You will work cross-functionally with Field Engineers, Operations, Commissioning, and Construction teams to ensure that data science solutions are grounded in operational reality and drive measurable impact. 
 
You will partner with engineering teams and program managers to define metrics, identify performance gaps, and build the analytical infrastructure needed to support strategic decisions at hyper-scale. You must be adept at operating in ambiguous, fast-moving environments where speed of insight can matter as much as analytical precision. 
 
The ideal candidate brings strong problem-solving skills, stakeholder communication skills, and the ability to balance technical rigor with delivery speed and customer impact. You will develop scalable analytical approaches to evaluate performance across the data center fleet to identify regional and site-specific insights, design and run experiments, and shape our development roadmap. You will build cross-functional support within the Data Center Community to assess business problems, define metrics, and support iterative scientific solutions that balance short-term delivery with long-term science roadmaps. 
 
Key job responsibilities 
<ul><li>Develop and maintain scalable models and analytical frameworks to measure and predict data center fleet performance, including availability, efficiency, and reliability KPIs across the global AWS infrastructure portfolio.</li><li>Apply advanced statistical and machine learning techniques to extract actionable insights from complex, large-scale operational datasets generated by data center systems (power, cooling, controls, etc.).</li><li>Partner with Field Engineers, Operations, and Portfolio Managers to identify high-impact opportunities for capacity and availability improvement, translating engineering domain knowledge into quantitative problem formulations.</li><li>Design and implement end-to-end data science workflows &mdash; from data acquisition and cleaning through model development, validation, and production deployment &mdash; enabling repeatable, scalable analysis.</li><li>Formalize assumptions about how data center systems are expected to perform and develop methods to systematically identify deviations, root causes, and high-ROI improvement opportunities.</li><li>Build self-service datasets, dashboards, and reporting mechanisms that provide Field Engineering leadership with real-time visibility into fleet health and portfolio performance.</li><li>Prepare narratives and data-driven recommendations for executive leadership that articulate decision points relative to fleet investment, risk trade-offs, and strategic priorities.</li><li>Collaborate with applied science, software engineering, and data engineering teams to ensure models integrate seamlessly with upstream and downstream systems.</li></ul> 
About the team 
Why AWS 
Amazon Web Services (AWS) is the world&rsquo;s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating &mdash; that&rsquo;s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses. 
 
Diverse Experiences 
Amazon values diverse experiences. Even if you do not meet all of the preferred qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn&rsquo;t followed a traditional path, or includes alternative experiences, don&rsquo;t let it stop you from applying. 
 
Work/Life Balance 
We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there&rsquo;s nothing we can&rsquo;t achieve in the cloud. 
 
Inclusive Team Culture 
Here at AWS, it&rsquo;s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon conferences, inspire us to never stop embracing our uniqueness. 
 
Mentorship and Career Growth 
We&rsquo;re continuously raising our performance bar as we strive to become Earth&rsquo;s Best Employer. That&rsquo;s why you&rsquo;ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional.</div><h2 class="jobSectionHeader">
BASIC QUALIFICATIONS</h2><ul><li>5+ years of data querying languages (e.g. SQL), scripting languages (e.g. Python) or statistical/mathematical software (e.g. R, SAS, Matlab, etc.) experience</li><li>4+ years of data scientist or similar role involving data extraction, analysis, statistical modeling and communication experience</li><li>Master's degree in a quantitative field such as Statistics, Mathematics, Data Science, Business Analytics, Economics, Engineering, or Computer Science; OR Bachelor's degree and 8+ years of professional experience in a quantitative role.</li></ul><h2 class="jobSectionHeader">
PREFERRED QUALIFICATIONS</h2><ul><li>2+ years of data visualization using AWS QuickSight, Tableau, R Shiny, etc. experience</li><li>Experience documenting modeling for technical and business leaders</li><li>Experience working with data engineers and business intelligence engineers collaboratively</li><li>Knowledge of AWS tech stack (e.g., AWS Redshift, S3, EC2, Glue)</li><li>Experience developing operational processes and data insights</li><li>Experience with anomaly detection, predictive maintenance, or reliability modeling in industrial or infrastructure contexts.</li></ul> 
Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status. 
 
Los Angeles County applicants: Job duties for this position include: work safely and cooperatively with other employees, supervisors, and staff; adhere to standards of excellence despite stressful conditions; communicate effectively and respectfully with employees, supervisors, and staff to ensure exceptional customer service; and follow all federal, state, and local laws and Company policies. Criminal history may have a direct, adverse, and negative relationship with some of the material job duties of this position. These include the duties and responsibilities listed above, as well as the abilities to adhere to company policies, exercise sound judgment, effectively manage stress and work safely and respectfully with others, exhibit trustworthiness and professionalism, and safeguard business operations and the Company&rsquo;s reputation. Pursuant to the Los Angeles County Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records. 
 
Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records. 
 
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. 
The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life &amp; AD&amp;D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits. 
USA, CA, San Francisco - 183,000.00 - 247,600.00 USD annually 
USA, WA, Bellevue - 159,200.00 - 215,300.00 USD annually 
USA, WA, Seattle - 159,200.00 - 215,300.00 USD annually</div>

Sr. Data Scientist, Field Engineering

Uber

<div>About the Role
 
 
As a Staff Data Scientist, you will serve as a strategic thought leader and &quot;Problem Finder&quot; for a high-growth business segment focused on the end-to-end traveler experience. This domain presents complex challenges, as user behavior often spans multiple markets, products, and timeframes, requiring non-trivial attribution, measurement, and experimentation frameworks. You will leverage your technical mastery and strategic influence to proactively identify, frame, and size the most ambiguous, high-leverage growth opportunities for the organization. This role demands sustained, high-leverage impact across the organization, setting the long-term technical direction for data science, and ensuring all critical decisions are grounded in rigorous data science.
 
 
What the Candidate Will Need / Bonus Points
 
 
What the Candidate Will Do 
 
 
<ul><li>Strategic Problem Finding: Proactively identify, frame, and quantify the most ambiguous, high-leverage growth opportunities for the business, translating them into actionable, organization-level projects.
</li><li>Lead End-to-End Solutions: Identify business opportunities and transform them into rigorous, data-driven solutions to build the next generation of end-to-end experiences for Uber travelers by partnering with engineering, product, and operations to co-drive execution.
</li><li>Advanced Experimentation Leadership: Design, own, and rigorously analyze complex experimentation frameworks, including those dealing with multi-market attribution, to provide definitive launch recommendations and set the scientific agenda for the team.
</li><li>Infrastructure &amp; Measurement Ownership: Define and champion the adoption of canonical business health metrics across the organization, partnering with data engineering to build scalable, reliable data and measurement infrastructure.
</li><li>Executive Influence: Synthesize complex data into clear, persuasive narratives and communicate strategic recommendations to senior leadership (Director/VP level), mobilizing cross-functional alignment across product, engineering, and operations.
</li><li>Cross-Functional Leadership: Partner deeply with engineering, product, and operations to identify impactful opportunities and co-drive execution from inception to delivery. Act as a scientific thought leader, mentoring junior scientists and influencing technical strategy across the organization.
</li><li>Technical Mentorship: Act as a scientific mentor, elevating the analytical and experimentation bar for junior and mid-level data scientists on the team. 
</li></ul> 
 
Basic Qualifications 
 
 
<ul><li>Bachelor's or M.S. degree in Economics, Operations Research, or a related quantitative field (e.g., Engineering, Applied Mathematics, Computer Science with a strong modeling focus).
</li><li>7+ years of relevant industry experience in a Product Data Science, Applied Science, or equivalent role, with a proven track record of driving organizational impact.
</li><li>Strong ability to understand user behavior from data, identify and size opportunities, and drive solutions end to end.
</li><li>Expert proficiency with Python, Pandas and SQL for large-scale data analysis, modeling, and production-level code collaboration.
</li><li>Expertise in advanced experimentation (A/B testing, causal inference) and applying statistical rigor to complex business problems.
</li><li>Exceptional communication skills, with proven experience in synthesizing complex data into persuasive narratives for both technical and executive audiences.
</li></ul> 
Preferred Qualifications 
 
 
<ul><li>PhD in Economics, Operations Research, or a related quantitative field (e.g., Engineering, Applied Mathematics, Computer Science with a strong modeling focus).
</li><li>Experience leading multi-quarter analytical workstreams with demonstrated, measurable impact at an organizational level.
</li><li>Expertise in two-sided marketplace dynamics (e.g., pricing, supply-demand balancing) and the measurement of network effects or cross-market spillovers.
</li><li>Proven track record of designing and analyzing advanced user behavior related experiments to design mass market products.
</li><li>Experienced in using AI/Agentic tools to accelerate data retrieval, analysis, and experimentation workflows, achieving a 10x improvement in speed and impact.
</li>Prior experience in a high-growth, high-scale technology or marketplace domain. 
<li>
</li></ul> 
 
 
 
For San Francisco, CA-based roles: The base salary range for this role is USD$216,000 per year - USD$240,000 per year. You will be eligible to participate in Uber's bonus program, and may be offered an equity award &amp; other types of comp. All full-time employees are eligible to participate in a 401(k) plan. You will also be eligible for various benefits. More details can be found at the following link https://jobs.uber.com/en/benefits.</div>

Staff Data Scientist, Airports and Travel

Data Scientist, Analytics (Technical Leadership)

Andiamo

<div>
 Data Scientist
 
 
 
 About The Role
 
 
 
 When you work in this role, you&rsquo;ll tackle tough problems alongside other scientists and engineers&mdash;people who will challenge your ideas, share knowledge, and collaborate on impactful projects. You&rsquo;ll be doing work that matters, developing solutions to complex real-world problems using data, invention, and rigorous inquiry.
 
 
 
 Responsibilities
 
 
 
 <ul>
 <li>
 Independently research and develop hypotheses based on diverse real-world datasets.
 </li>
 <li>
 Conduct literature reviews to apply cutting-edge methodologies for extracting meaningful signals from data.
 </li>
 <li>
 Partner with engineers and stakeholders to rigorously explore and test your theories.
 </li>
 <li>
 Stay engaged in the academic community by joining reading circles and attending seminars to keep up with the latest research.
 
 
 </li>
 </ul>
 
 Qualifications
 
 
 
 <ul>
 <li>
 1&ndash;5+ years of experience in applied data analysis and prediction, preferably in an industry setting.
 </li>
 <li>
 Degree in a technical or quantitative discipline such as statistics, mathematics, physics, electrical engineering, or computer science.
 </li>
 <li>
 Proficiency in Python and SQL.
 </li>
 <li>
 Experience conducting in-depth research projects involving real-world data.
 </li>
 <li>
 Ability to think independently, approach analysis creatively, and communicate complex ideas clearly.
 </li>
 <li>
 Background in finance is not required&mdash;strong research skills are most important.
 
 
 </li>
 </ul>
 
 Benefits
 
 
 
 <ul>
 <li>
 Core Benefits: Fully paid medical and dental insurance premiums for employees and dependents, competitive 401k match, employer-paid life and disability insurance.
 </li>
 <li>
 Perks: Onsite gyms with laundry service, wellness activities, casual dress, snacks, game rooms.
 </li>
 <li>
 Learning: Tuition reimbursement, conference and training sponsorship.
 </li>
 <li>
 Time Off: Generous vacation and unlimited sick days, competitive paid caregiver leaves.
 </li>
 <li>
 Hybrid Work Policy: Flexible in-office days with budget for home office setup.
 
 
 </li>
 </ul>
 
 Compensation
 
 
 
 The base pay for this role will be between
 
 $165,000 and $300,000
 
 . This role may also be eligible for additional forms of compensation and benefits such as discretionary bonuses, health and wellness plans, and 401(k) contributions. Discretionary bonus can be a significant portion of total compensation. Actual compensation will depend on skills, qualifications, and experience.
 
 
 
 About Andiamo
 
 
 
 Talent Partners for the AI Revolution. As a globally recognized staffing and consulting firm, we specialize in placing the top 2% of technology and go-to-market professionals with the world&rsquo;s largest and most well-known companies.
 
 
 For over 20 years, we've maintained the status of tier-one vendor for firms such as Palantir, Amazon, Fluidstack, Bloomberg, Relativity Space, Firefly, MasterCard, Visa, Two Sigma, Citadel, as well as other major financial services firms, elite hedge funds, Google-backed tech start-ups, and major software firms.
 
 
 Our talent solutions include Permanent Placement, Contract Staffing, Executive Search, and Dedicated Recruiting Services (RPO). Find out more at www.andiamogo.com
</div>

Data Scientist - Data-Driven Global Investment Firm

Samba TV

<div>
 
 
 Samba is a media intelligence company. We know what the world is watching, reading, and thinking about &amp;mdash; in real time, at scale, across every screen. Our data exists with the consent of over a billion people, organized into the most complete picture of consumer attention ever built. The biggest brands in the world use that picture to make smarter decisions. We think it&rsquo;s the most interesting data asset on the planet, because it&rsquo;s the most culturally relevant.&nbsp;
 
 
 
 
 
 
 
 
 
 
 ABOUT THE ROLE
 
 
 
 
 
 
 We are looking for a hands-on Data Scientist to own and deliver complex measurement science and modeling work at the core of our measurement and audience sciences products.&nbsp;
 
 
 
 
 
 The role requires a deep, first-principles understanding of data science and machine learning &amp;mdash; not just the ability to apply libraries, but the ability to reason clearly about model behavior, articulate trade-offs between approaches, and make defensible methodological decisions under ambiguity. This is emphatically a coding role &amp;mdash; you will spend the majority of your time writing production-quality Python, building and evaluating models on large-scale viewership and web data, and delivering end-to-end ML solutions.
 
 
 
 
 
 You will work closely with Data Engineering, Product, and go-to-market teams.
 
 
 
 
 
 
 
 
 
 WHAT YOU'LL DO
 
 
 <ul>
 
 <li>
 
 
 
 Write and own production-quality Python code end-to-end &amp;mdash; well-structured, tested, documented, and built to last; PySpark proficiency is essential for working with Samba's billion-row viewership datasets
 
 
 
 </li>
 
 <li>
 
 
 
 Design, build, and deploy measurement models and statistical frameworks that power Samba&rsquo;s campaign measurement, reach/frequency estimation, and cross-platform attribution products
 
 
 
 </li>
 
 <li>
 
 
 
 Apply the right statistical and ML technique to the right problem &amp;mdash; drawing from hierarchical models, Bayesian inference, gradient boosting, regularized regression, causal ML, and probabilistic record linkage &amp;mdash; and clearly articulate the reasoning behind your choices
 
 
 
 </li>
 
 <li>
 
 
 
 Build and evaluate multi-touch and multi-channel attribution models; apply Causal ML methods &amp;mdash; counterfactual modeling, meta-learners (S-learner, T-learner, X-learner), and heterogeneous treatment effect estimation &amp;mdash; to advertising and viewership measurement problems
 
 
 
 </li>
 
 <li>
 
 
 
 Partner with Data Engineering to define data requirements, validate pipelines, and ensure model inputs are reliable, scalable, and production-ready
 
 
 
 </li>
 
 <li>
 
 
 
 Lead technical design reviews and contribute meaningfully to architecture decisions across the Data Science team
 
 
 
 </li>
 
 <li>
 
 
 
 Mentor junior Data Scientists through code review, pairing, and structured technical feedback &amp;mdash; raising the team's technical floor
 
 
 
 </li>
 
 <li>
 
 
 
 Communicate measurement methodologies and findings clearly to technical and non-technical audiences, including senior leadership and external clients
 
 
 
 </li>
 
 </ul>
 
 
 
 
 
 WHO ARE YOU
 
 
 <ul>
 
 <li>
 
 
 
 5-7 years of professional data science experience &amp;mdash; hands-on, delivery-focused, and measurable in shipped models and production systems
 
 
 
 </li>
 
 <li>
 
 
 
 Expert-level Python &amp;mdash; clean, modular, testable, production-ready code is your standard, not your aspiration
 
 
 
 </li>
 
 <li>
 
 
 
 Advanced PySpark and Databricks &amp;mdash; comfortable building and optimizing data pipelines and ML workflows on billion-row datasets
 
 
 
 </li>
 
 <li>
 
 
 
 Deep, first-principles command of statistics and ML &amp;mdash; you can explain from the ground up how these models work and you apply this understanding to make better modeling decisions
 
 
 
 </li>
 
 <li>
 
 
 
 Solid grasp of experimental design &amp;mdash; A/B testing, randomization, power analysis, and the conditions under which observational causal inference is appropriate
 
 
 
 </li>
 
 <li>
 
 
 
 Fluent in the full ML lifecycle: feature engineering, model evaluation, deployment pipelines, drift monitoring, and iterative improvement in production
 
 
 
 </li>
 
 <li>
 
 
 
 Hands-on experience with uplift modeling, synthetic control, difference-in-differences, or propensity-based approaches applied to advertising or media outcomes
 
 
 
 </li>
 
 <li>
 
 
 
 Strong ownership mindset &amp;mdash; you drive projects independently and are comfortable owning your models from data exploration through production delivery, with minimal hand-holding.
 
 
 
 </li>
 
 <li>
 
 
 
 Clear communicator &amp;mdash; able to translate statistical reasoning and model behavior into language that drives decisions with product, engineering, and leadership
 
 
 
 </li>
 
 </ul>
 
 
 <ul>
 
 <li>
 
 
 
 Experience with multi-touch attribution (MTA) or multi-channel attribution modeling &amp;mdash; understanding of the limitations of rule-based approaches and the methodological trade-offs of data-driven alternatives
 
 
 
 </li>
 
 <li>
 
 
 
 Hands-on experience with Causal ML methods &amp;mdash; counterfactual modeling, meta-learners, and heterogeneous treatment effect estimation &amp;mdash; applied to advertising or media measurement outcomes
 
 
 
 </li>
 
 <li>
 
 
 
 Direct exposure to TV or digital viewership data &amp;mdash; ACR signals, STB data, viewership panels, or cross-platform measurement (linear + CTV/OTT)
 
 
 
 </li>
 
 <li>
 
 
 
 Familiarity with the measurement
 
 
 
 </li>
 
 <li>
 
 
 
 t vendor landscape (Nielsen, Comscore, VideoAmp, iSpot) and industry standards (MRC, GRP/TRP frameworks)
 
 
 
 </li>
 
 <li>
 
 
 
 Advanced degree (MS or PhD) in Statistics, Mathematics, Computer Science, or a related quantitative field &amp;mdash; or equivalent depth demonstrated through work
 
 
 
 </li>
 
 </ul>
 
 
 
 
 
 
 
 
 
 Samba is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.&nbsp;&nbsp;We strive to empower connection with one another, reflect the communities we serve, and tackle meaningful projects that make a real impact.
 
 
 
 
 &nbsp;
 
 
 
 
 Samba may collect personal information directly from you, as a job applicant, Samba may also receive personal information from third parties, for example, in connection with a background, employment or reference check, in accordance with the applicable law.
 
 
 For further details, please see Samba's
 
 
 Applicant Privacy Policy
 
 
 .
 
 
 For residents of the EU , Samba Inc. is the data controller.
 
 
 
 
 We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
 
 
 
 Samba expects to offer a base salary between
 
 $150,000 - $180,000 USD
 
 per year for roles to be performed in San Francisco or California; actual base salary offered will depend on various factors including but not limited to location, experience, and performance. Base salary is just one component of Samba's total compensation package for employees. Other rewards may include bonuses, short-term incentives and long-term incentives. In addition, Samba provides health insurance, wellness offerings, life and disability insurance, a retirement savings plan, paid holidays and paid time off (PTO), and other employee benefits.
</div>

Data Scientist

Verse

<div>
 
 Location:
 
 San Francisco, CA (Hybrid)
 
 
 
 What is Verse?
 
 
 
 Energy markets are more volatile than ever. Rapid electrification and the rise of AI are driving unprecedented demand for power, while energy costs continue to rise across the globe. For the world&rsquo;s largest energy buyers, managing energy has never been more complex or more critical.
 
 
 Verse helps these organizations manage complex power portfolios with confidence by unifying energy data, planning, forecasting, and operations in one tool. Our Energy Cost Intelligence platform, Aria, brings together energy, finance, and operations teams with real-time, finance-ready intelligence&mdash;replacing spreadsheets and consultants with precision across the entire energy lifecycle. Built by an expert team of energy buyers, data scientists, and engineers, Verse enables faster, smarter energy decisions that reduce risk and lower energy costs.
 
 
 
 The Role
 
 
 
 Verse is seeking a Data Scientist to join our Data Science Team. In this role, you will lead the development and deployment of advanced data-driven solutions across a range of applications, including electricity markets, renewable procurement, and energy risk management. You will shape the machine learning and data modeling foundations that Verse's software is built on. For example, you might spend a cycle deploying electricity market price forecasting pipelines for new regions, developing solar production anomaly detection models, or creating scalable tools for benchmarking energy project financial performance.
 
 
 
 Key Responsibilities
 
 
 
 <ul>
 <li>
 
 Lead End-to-End Data Science Projects:
 
 Own and drive projects from problem definition through scoping, modeling, validation, and production deployment. Translate business problems into scalable, high-impact modeling solutions.
 </li>
 <li>
 
 Statistical &amp; Machine Learning Modeling:
 
 Design, develop, and refine statistical and machine learning models (e.g., time series forecasting, probabilistic models) to support decision-making and enhance product capabilities.
 </li>
 <li>
 
 Analytics Engineering &amp; Data Modeling:
 
 Perform complex data transformations and develop well-structured data models. Translate business and analytical requirements into scalable, tested, and well-documented datasets.
 </li>
 <li>
 
 Software Development &amp; Productionization:
 
 Write clean, efficient, and maintainable Python code. Contribute to integrating models into production systems in a cloud-based environment while leveraging AI coding tools to accelerate development.
 </li>
 <li>
 
 MLOps:
 
 Contribute to Verse&rsquo;s machine learning modeling infrastructure to support scaling of ML models and improving reliability, monitoring, and performance in production.
 </li>
 <li>
 
 Cross-Functional Collaboration:
 
 Partner with product, engineering, and business stakeholders to ensure models and insights are aligned with user needs and effectively integrated into workflows.
 
 
 </li>
 </ul>
 
 What We're Looking For (Minimum Qualifications)
 
 
 
 <ul>
 <li>
 Master&rsquo;s degree in Computer Science, Statistics, Engineering, Applied Mathematics, or a related quantitative field and 2+ years of professional experience in data science or machine learning; or
 </li>
 <li>
 Bachelor&rsquo;s degree and 4+ years of professional experience in data science or machine learning
 </li>
 <li>
 Strong foundation in statistical modeling and machine learning, including time series forecasting and model evaluation
 </li>
 <li>
 Experience deploying and maintaining models in cloud-based environments (e.g., AWS, GCP, or Azure) using MLOps practices
 </li>
 <li>
 Strong Python expertise, including experience with scientific computing and ML libraries (e.g., NumPy, pandas, scikit-learn, PyTorch, TensorFlow)
 </li>
 <li>
 Hands-on experience in orchestrating complex data transformations (e.g. Airflow, Dagster, dbt)
 </li>
 <li>
 Strong software engineering practices (version control, testing, code reviews, CI/CD)
 </li>
 <li>
 Strong communication skills, with the ability to explain technical concepts to non-technical stakeholders
 
 
 </li>
 </ul>
 
 What Will Make You Standout (Preferred Qualifications)
 
 
 
 <ul>
 <li>
 Experience in energy, climate tech, or related domains (not required)
 </li>
 <li>
 Familiarity with optimization methods or operations research
 </li>
 <li>
 Experience developing probabilistic forecasting models and quantifying uncertainty
 </li>
 <li>
 PhD in a quantitative field
 
 
 </li>
 </ul>
 
 What Makes Verse a Great Place to Work?
 
 
 
 <ul>
 <li>
 
 Lead with Empathy:
 
 We lift each other up with humility and kindness, always putting colleagues and customers first
 </li>
 <li>
 
 Be Honest &amp; Transparent:
 
 We prioritize effective communication to build trust with our team, customers, and stakeholders
 </li>
 <li>
 
 Move with Balance &amp; Precision:
 
 We believe speed and perseverance must be accompanied by thoughtfulness and reflection
 </li>
 <li>
 
 Leave the World a Better Place:
 
 We are passionate about our mission, and we strive to create a sustainable world for future generations
 
 
 </li>
 </ul>
 
 Base Pay Range
 
 
 
 $146,000 - $172,000
 
 
 
 This is the estimated base salary range for this position, which does not include the value of benefits or a potential equity grant. A wide range of factors are considered in making compensation decisions, including but not limited to skill sets, market conditions, experience and training, licensure and certifications, and business and organizational needs.
 
 
 
 
 Benefits and Employee Perks
 
 
 
 <ul>
 <li>
 Competitive compensation and equity grant at a high-growth start up
 </li>
 <li>
 Comprehensive benefits package including medical, dental and vision insurance, and 401k
 </li>
 <li>
 Flexible hours and unlimited PTO
 </li>
 <li>
 Diverse and inclusive working environment
 
 
 </li>
 </ul>
 Verse is an equal opportunity employer. All applicants and employees are considered for hire, promotion, and compensation without regard to race, color, religion, sex, national origin, age, disability, sexual orientation, marital or familial status.
</div>

Google

<div>
 Applicants in San Francisco: Qualified applications with arrest or conviction records will be considered for employment in accordance with the San Francisco Fair Chance Ordinance for Employers and the California Fair Chance Act.
 
 
 Note: By applying to this position you will have an opportunity to share your preferred working location from the following:
 
 San Francisco, CA, USA; Reston, VA, USA
 
 .
 
 Minimum qualifications:
 
 
 
 <ul>
 <li>
 Master's degree in Statistics, Data Science, Mathematics, Physics, Economics, Operations Research, Engineering, or a related quantitative field.
 </li>
 <li>
 3 years of work experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or a PhD degree.
 
 
 </li>
 </ul>
 
 Preferred qualifications:
 
 
 
 <ul>
 <li>
 3 years of experience developing and deploying machine learning and AI models in production settings.
 </li>
 <li>
 Experience at the intersection of security or fraud and ML.
 </li>
 <li>
 Experience applying a variety of unsupervised, semi-supervised, and supervised machine learning techniques, and the ability to turn big data into actionable intelligence.
 </li>
 <li>
 Experience building LLM-powered applications to automate the analysis and contextualization of complex data.
 </li>
 <li>
 Experience with the challenges in applying machine learning in a non-stationary and adversarial environment.
 </li>
 <li>
 Ability to evaluate, analyze, and improve machine learning models and LLM-driven systems.
 
 
 </li>
 </ul>
 
 About The Job
 
 
 
 The Google Cloud Gusto AI Data Science Research team develops innovative, data-driven solutions to today&rsquo;s most challenging cybersecurity problems. By leveraging data derived from Google's unparalleled view of the threat landscape, our cross-functional, applied research team experiments and delivers impactful findings for both our customers and the broader cybersecurity industry.
 
 
 We are looking for a Research Data Scientist to help us solve challenging cybersecurity problems and protect billions of customers by applying their expert knowledge of machine learning, statistics, and Generative AI. In this role, you will partner with subject matter experts on the front lines defending against advanced threat actors, iteratively develop new capabilities, and work closely with our engineering team to deploy and maintain them. Our team works on a wide variety of cybersecurity challenges including agentic security operations, threat intelligence contextualization, security-tuning LLMs, dark web threat detection, and more. We encourage sharing impactful learnings through publications at academic conferences and talks at industry venues.
 
 
 The US base salary range for this full-time position is $147,000-$211,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.
 
 
 Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google .
 
 
 
 Responsibilities
 
 
 
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 Explore promising areas of future research at the intersection of cybersecurity and machine learning.
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 Drive the research and development of new models and analytic products to solve cybersecurity problems.
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 Develop models and analytics. Move from proof of concept to minimum viable product quickly and efficiently.
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 Work closely with other engineering teams to develop scalable data pipelines, deploy models/analytics, and enact telemetry-driven model improvements over time.
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 Communicate research results to stakeholders and the research community through documentation, white papers, peer-reviewed publications, and presentations.
 
 
 
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 Google is proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. See also Google's EEO Policy and EEO is the Law. If you have a disability or special need that requires accommodation, please let us know by completing our Accommodations for Applicants form .
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Research Data Scientist, Cybersecurity, Google Cloud

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Research Data Scientist, Cybersecurity, Google Cloud