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data scientist product analytics

Data Scientist, Product Analytics

Decagon

<div>
 
 About Decagon
 
 
 
 Decagon is the leading conversational AI platform empowering every brand to deliver concierge customer experiences.
 
 
 Our technology enables industry-defining enterprises like Avis Budget Group, Block&rsquo;s Cash App and Square, Chime, Oura Health, and Hunter Douglas to deploy AI agents that power personalized, deeply satisfying interactions across voice, chat, email, SMS, and every other channel.
 
 
 We&rsquo;re building a future where customer experiences are being redefined from support tickets and hold music to faster resolutions, richer conversations, and deeper relationships. We&rsquo;re proud to be backed by world-class investors who share that vision, including a16z, Accel, Bain Capital Ventures, Coatue, and Index Ventures, along with many others.
 
 
 We&rsquo;re an in-office company, driven by a shared commitment to excellence and velocity. Our values &mdash; Just Get It Done, Invent What Customers Want, Winner&rsquo;s Mindset, and The Polymath Principle &mdash; shape how we work and grow as a team.
 
 
 
 About The Role
 
 
 
 As a Data Scientist at Decagon, you&rsquo;ll bring deep technical rigor to how data is analyzed, tested, and applied across the business. You&rsquo;ll design and interpret experiments, apply advanced statistical methods, and build scalable analytical frameworks that directly inform product strategy and company performance.
 
 
 You&rsquo;ll work across Engineering and Product to establish robust measurement practices, expand the use of data-driven decision making, and uncover insights that shape Decagon&rsquo;s trajectory. This role blends strong analytical expertise with product acumen and the ability to influence at the highest levels.
 
 
 
 In this role, you will
 
 
 
 <ul>
 <li>
 Build and maintain foundational datasets and metrics that reflect infrastructure usage, system performance, and scaling efficiency across Decagon's AI platform.
 </li>
 <li>
 Enhance and extend Decagon&rsquo;s data models, metrics, and dashboards to ensure consistent, scalable reporting across teams.
 </li>
 <li>
 Partner closely with Engineering to shape strategy through data &mdash; surfacing bottlenecks, inefficiencies, and scaling risks before they become problems.
 </li>
 <li>
 Collaborate with Product and Engineering to embed experimentation and measurement best practices into product development cycles.
 </li>
 <li>
 Build and continuously improve Decagon&rsquo;s data stack, ensuring scalable models, pipelines, and tools that empower self-serve analytics across the company.
 </li>
 <li>
 Communicate findings clearly and persuasively to executives and cross-functional stakeholders, influencing strategic decision-making.
 </li>
 <li>
 Conduct deep-dive quantitative investigations to support incident analysis, reliability improvements, and post-mortem learnings.
 </li>
 <li>
 Evaluate and interpret the performance of ML models (e.g., classification, recommendation, LLM-based systems), helping the team reason through trade-offs, limitations, and business implications of model-driven decisions.
 </li>
 <li>
 Be the primary driver of experimentation culture at Decagon, helping define how we design, measure, and scale experiments across the business.
 
 
 </li>
 </ul>
 
 What We're Looking For
 
 
 
 <ul>
 <li>
 Strong ability to structure ambiguous problems, translate them into analytical approaches, and deliver business impact through data.
 </li>
 <li>
 Advanced proficiency in SQL and Python, with a solid foundation in statistics and experimental design.
 </li>
 <li>
 5+ years (or equivalent experience) in data science or product analytics roles, ideally within high-scale consumer or marketplace products.
 </li>
 <li>
 Ability to reason about and analyze machine learning models, understanding their assumptions, trade-offs, and impact on product and customer outcomes.
 </li>
 <li>
 Proven track record as a data partner in large-scale backend or infrastructure systems.
 </li>
 <li>
 Comfort building operational analytics (e.g., alerting, SLIs/SLOs) and partnering closely with data and infra engineering.
 </li>
 <li>
 Excellent communication skills, with experience influencing stakeholders and driving decisions across teams.
 
 
 </li>
 </ul>
 
 Benefits
 
 
 
 <ul>
 <li>
 Medical, dental, and vision benefits
 </li>
 <li>
 Take what you need vacation policy
 </li>
 <li>
 Daily lunches, dinners and snacks in the office to keep you at your best
 
 
 </li>
 </ul>
 
 Compensation
 
 
 
 $250k - $300k + equity
</div>

Senior Data Scientist

Owner.com

<div>About Owner.com
Owner is the AI growth system for local restaurants. 
Our AI continuously improves SEO, marketing, and online ordering to grow first-party orders. Unlike other companies that force small business owners to master their software to drive sales, Owner gives them a proven system run by experts. 
Owner is like having an army of engineers and marketers on your side, just like the big chains. 
Our vision
We&rsquo;re starting by helping independent restaurants succeed online. 
But it&rsquo;s not just restaurants that need our help. Most local businesses are struggling with these same problems. Huge technology corporations are taking their customers, bleeding their profits, and making it hard for them to survive. 
Once we nail the solution for restaurants &ndash; we&rsquo;ll scale it into every other local business type. 
In the future we envision, tens of millions of local business owners will use our technology to succeed in the digital age. 
Read our Series C memo here 
Our traction
Since 2020, we've generated tens of millions in revenue and processed over half a billion dollars of online orders. 1 in 5 Americans have used an Owner.com website. 
More importantly, we&rsquo;ve helped over 20,000 restaurant owners, and saved them nearly $200 million in fees. 
Our team
Our team is now in the low hundreds. We&rsquo;ve got top talent from the most successful companies in SMB software, including: Shopify, HubSpot, DoorDash, ServiceTitan, Rappi, Faire and Stripe. 
We&rsquo;ll be scaling even faster in 2026 to keep pace with our customer growth. 
Where we work
Owner is a remote-first, global company headquartered in San Francisco, with a sales hub in Toronto. For a few of our roles we prioritize in-person collaboration at one of our office locations. Most of our teammates are distributed throughout the globe. Please review the role description and discuss with your recruiter for more details on location. 
Why we&rsquo;re looking for you
We&rsquo;re building an effective, impactful Product Analytics function at Owner.com. As a Risk &amp; Payments Data Scientist, you will play a pivotal role in shaping the product roadmap through close collaboration with Product Managers to establish proper metrics and impact sizing. You will drive experimentation initiatives and manage the testing framework. You will be instrumental in designing and implementing KPIs for our payments product squad, identifying gaps and potential opportunities that will help grow the business. The best candidates will not just provide insights to the EPD (Engineering &amp; Product) org, but be a strategic driver, identifying gaps/opportunities in the money &amp; risk area, building internal alignment around them, and creating meaning business impact.
We&rsquo;re migrating from Stripe Standard to Custom Connect / Adyen for Platforms, which means we&rsquo;ll own far more of the risk surface: merchant onboarding/underwriting, fraud detection, dispute mitigation, reserves, and payout risk. As a Risk Data Scientist you will be building the models, signals, and decisioning that protect our merchants and our P&amp;L&mdash;while keeping conversion high and friction low. 
For this particular role we are focused on candidates located in the San Francisco Bay Area. We are a remote-first company with a home base in SF, where our team comes together for periodic in-person collaboration (most local teammates opt to come in on Tuesdays/Thursdays). For more details chat with your recruiter!
The impact you will have<ul><li>
Payments modeling:</li><li>
Cut losses without killing conversion: Ship ML models that reduce fraud/chargebacks and credit losses while maintaining checkout auth rates and onboarding pass-through.</li><li>
Accelerate safe growth: Create merchant risk scores and dynamic controls (e.g., reserves/holdbacks, payout delays) that scale to 10k+ restaurants.</li><li>
Give Ops superpowers: Build signals, alerts, and tools that let our Payments Ops / Risk Ops team review what matters&mdash;and automate the rest.</li><li>
Make risk measurable: Define loss budgets and risk SLIs/SLOs; deliver dashboards that make risk tradeoffs explicit.</li><li>
Build and maintain ML models for merchant underwriting, transaction fraud, chargeback propensity, payout risk.</li><li>
Design reusable frameworks for feature generation, model training, deployment, and monitoring so we can add new models quickly without reinventing the wheel.</li><li>
Payments analytics:</li><li>
Own analytics for payments, billing, and risk features, from user checkout experience to internal financial reporting.</li><li>
Monitor and improve critical KPIs such as payment success rate, failed payment recovery, fraud rates, chargeback volume, and revenue leakage. Set up monitoring for drift, stability, and business KPIs, with automated alerts.</li><li>
Identify and size revenue &amp; risk opportunities across the payments funnel (from checkout to Stripe to invoice collection).</li><li>
Partner with Product Managers on AB tests and experiments related to payments UX, fraud flags, or risk workflows.</li><li>
Collaborate with Engineering to instrument new product features and ensure great event tracking and data integrity in payment flows.</li><li>
Enhance product planning influencing product planning through informative impact sizing, enabling more strategic decision-making.</li><li>
Improve data Integrity and quality: Collaborate with developers on database design to strengthen data integrity and quality.</li><li>
Establish a Single Source of Truth (SSOT): Work alongside Data &amp; Analytics Engineers to implement robust models in DBT and Snowflake, and design dashboards that provide a unified view of business-critical data. Integrate third-party and processor signals (Stripe Radar, Adyen RevenueProtect, device/identity data) into our models.</li></ul>
Who you&rsquo;ll work with<ul><li>
Reporting Structure: This role reports directly to our VP, Business Operations, Jonathan Shenkman.</li><li>
Technical Collaboration: You will collaborate with Analytics Engineers on all technical aspects, including data modeling, data quality, and the use of tools like DBT and Snowflake.</li><li>
Work hand-in-glove with Payments Ops &amp; Risk Lead to encode policy into models, define review queues, and reduce manual workload.</li><li>
Collaborate with Payments PM/GM on onboarding UX, step-up flows, and dispute tooling; quantify conversion vs. loss tradeoffs with clear, dollarized impact.</li><li>
Provide merchant-level insights (watchlists, risk cohorts) and playbooks (what to hold, what to terminate, what to educate).</li></ul>
What we're looking for<ul><li>
4&ndash;8+ years in applied ML or risk data science (fintech/payments, marketplace, or anti-fraud).</li><li>
Hands-on with Python, SQL, and ML libraries/frameworks; comfortable with MLflow (or equivalent), and feature stores.</li><li>
Proven track record shipping production models that materially reduced losses or improved conversion; strong offline evaluation + online experimentation skills.</li><li>
Strong understanding of MLops and experience deploying MLops systems</li><li>
Experience deploying machine learning models to an api for realtime predictions</li><li>
Experience building systems for model observability after deployment</li><li>
Deep familiarity with payments/risk concepts: KYC/KYB, underwriting, auth vs capture, chargebacks, friendly fraud, card testing, reserves, payout returns, soft/hard declines.</li><li>
Strength in feature engineering on messy, imbalanced data; rigorous cost-sensitive evaluation (ROC/PR, cost curves, business impact).</li><li>
Excellent communicator who can turn model output into clear decisions and dollarized tradeoffs.</li><li>
Strong grasp of metrics design, experimentation, and product funnel analysis.</li><li>
Ability to handle ambiguity, deep dive into financial systems, and proactively flag problems before they escalate.</li><li>
KPI Development: A proven track record of developing KPIs and metrics tailored for product squads, particularly within a startup environment.</li><li>
Data Product Expertise: Ability to build comprehensive end-to-end data products.</li><li>
Organizational Skills: Highly organized with a keen eye for precision.</li><li>
Industry Experience: Prior experience in SaaS and/or startup environments is highly preferred. Experience in the restaurant industry is an added advantage.</li></ul>
Pay and benefits<ul><li>
The estimated base salary range for this role is $200k - $240k, plus a generous equity pre-IPO equity package</li><li>
Other benefits include comprehensive health coverage, unlimited PTO - plus extra fun perks!</li></ul>
Notice - Employment Scams
Communication from our team regarding job opportunities will only be made by an Owner team member with an @owner.com email address.
We do not conduct interviews over email or chat platforms, and we will never ask you to provide personal or financial information such as your mailing address, social security number, credit card numbers or banking information. If you believe you are being contacted by scammer, please mark the communication as &quot;phishing&quot; or &ldquo;spam&rdquo; and do not respond.</div>

Staff Product Data Scientist, Risk and Payments

Uber

<div>We're looking for PhD candidates to work with the Competitive Intelligence Team as a Scientist intern during summer 2026 (12 weeks). As an intern, you will be embedded in a product team working on solving real-world Uber problems under the supervision of an analyst on that team, and will have the opportunity to partner closely with Scientists, Software Engineers, Product Managers, and other cross-functional partners.
 
 
About the Role
 
 
The Competitive Intelligence team works to understand the strategy and performance of Uber and other companies with similar products through a combination of external and internal data. Uber operates in a diverse array of markets and product categories filled with rapidly evolving competing options; assessing the success or failure of a product or strategy depends on understanding this context. Doing this, and doing it well, is an interesting and challenging problem. The team uses its data analysis, statistical modeling, economics, and business expertise to build scalable data products and ship actionable insights.
 
 
We are looking for candidates passionate about solving new and difficult problems with data. You will build out and own competitive metrics/models, provide key insights, conduct deep dive analysis to understand new opportunities and empower teams to make more informed business and strategy decisions with competitive intelligence.
 
 
What You'll Do
<ul><li>Work with a mentor closely to define a business problem, scope a project, develop, and prototype the solution using data-driven approaches
</li><li>Work with engineers and product managers to turn prototypes into scalable solutions
</li><li>Present findings to leaders to inform decisions
</li><li>Establish standard methodologies for science such as modeling, coding, analytics, optimization, and experimentation
</li></ul>Basic Qualifications
<ul><li>Pursuing a Ph.D. majoring in Statistics, Machine Learning, Economics, or other related quantitative fields
</li><li>Candidates should have at least one semester/quarter left of their education after finishing the internship
</li><li>Strong problem-solving and analytical abilities
</li></ul>Preferred Qualifications
<ul><li>Coding proficiency in areas such as R, Spark, SQL, Python
</li><li>Background in data visualization via open-source libraries/packages or third-party tools (i.e. Tableau, Mixpanel, Looker, or similar)
</li><li>Knowledge of underlying mathematical foundations of statistics, machine learning, optimization, stochastic processes, economics, and analytics
</li><li>Experience in the following areas: Exploratory Data Analysis, Statistical Analysis, ML Model Development, Competitive Intelligence, Causal Inference, Operations Management, Transportation
</li><li>0-2 years of prior work experience in an analytical setting
</li><li>Organized, detail-oriented and able to work independently on multiple projects at once
</li><li>Ability to communicate effectively with both technical and business partners
</li><li>Open to feedback, excellent at implementing newly learned ideas and concepts
</li><li>Research mentality with a bias towards action to structure a project from idea to prototype to implementation
</li>Independence, self-starter mindset, excellent communication, and outstanding follow-through - you energetically tackle your work and love the responsibility of being individually empowered 
<li>
</li></ul> 
 
For San Francisco, CA-based roles: The base hourly rate amount for this role is USD$67.00 per hour. For Seattle, WA-based roles: The base hourly rate amount for this role is USD$67.00 per hour. For Sunnyvale, CA-based roles: The base hourly rate amount for this role is USD$67.00 per hour. For all US locations, you will also be eligible for various benefits.</div>

2026 PhD Scientist Intern (Competitive Intelligence)

<div>About the Role
 
 
Have you ever wondered why it's taking so long for an earner to be matched to your trip, why the ETA is so long, or how an Earner is picked from the many around you? If so, the Mobility Matching Science team is for you!
 
 
The Matching team at Uber builds the systems that determine the optimal way to fulfill trips on the Mobility platform. We work on the problems of determining which earners to send an offer to and when. The solutions we build are critical for maintaining reliability and ensuring the trust of riders and earners alike.
 
 
We are looking for experienced scientists who relish the opportunity to develop novel approaches and apply them at Uber's scale. They ideally have a good balance of causal inference, analysis, experimentation, and modeling knowledge, as well as, an ability to use these skills to identify business opportunities and deliver product recommendations.
 
 
What You'll Do
<ul><li>Develop data-driven business insights and work with cross-functional stakeholders to identify opportunities and recommend prioritization of product, growth and optimization initiatives
</li><li>Design and analyze experiments, communicating results that draw detailed and actionable conclusions
</li><li>Analyze and contribute to development of optimization algos and ML models for use in mobility matching
</li><li>Collaborate with cross-functional teams such as product, engineering and operations to drive system development end-to-end from conceptualization to final product
</li></ul>Basic Qualifications
<ul><li>Ph.D. in Statistics, Economics, Machine Learning, Operations Research, Computer Science, or another quantitative field.
</li><li>Minimum 2 years of industry experience as an Applied Scientist, Data Scientist, or in a similar quantitative role.
</li><li>Strong knowledge of the mathematical foundations of statistics, machine learning, optimization, and economics.
</li><li>Proven experience in experimental design (e.g., A/B testing) and causal inference.
</li><li>Proficiency in using Python or R for data analysis, modeling, and algorithm prototyping at scale with large datasets.
</li><li>Experience with exploratory data analysis, statistical analysis and testing, and model development.
</li></ul>Preferred Qualifications
<ul><li>Ph.D., or M.S. in Statistics, Economics, Machine Learning, Operations Research, Computer Science, or another quantitative field.
</li><li>Minimum 5 years of industry experience as an Applied Scientist, Data Scientist, or in a similar quantitative role.
</li><li>Strong knowledge of the mathematical foundations of statistics, machine learning, optimization, and economics.
</li><li>Proven experience in experimental design (e.g., A/B testing) and causal inference.
</li><li>Proficiency in using Python or R for data analysis, modeling, and algorithm prototyping at scale with large datasets.
</li>Experience with exploratory data analysis, statistical analysis and testing, and model development. 
<li>
</li></ul> 
 
For San Francisco, CA-based roles: The base salary range for this role is USD$190,000 per year - USD$211,000 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>

Sr. Scientist - Mobility Matching

Tatari

<div>
 Tatari is on a mission to revolutionize TV advertising. Founded in 2016 to help transform the antiquated world of TV advertising through the intelligent application of AI and machine learning, Tatari helps some of the world&rsquo;s fastest growing brands including
 
 Chime, Calm, Tecovas, Manscaped, Saatva, and Liquid I.V
 
 ., reach their customers using linear and streaming TV ads. Our platform combines sophisticated media buying with proprietary analytics to turn TV advertising into an automated, digital-like experience, enabling businesses of any size to advertise on TV.
 
 
 That approach has earned Tatari broad industry recognition, including being named Best CTV AdTech Platform in the 8th annual
 
 MarTech Breakthrough Awards
 
 , as well as honors from
 
 Digiday
 
 (Best Connected TV Platform),
 
 AdExchanger
 
 (Most Innovative TV Advertising Technology), and
 
 Business Insider
 
 (Hottest AdTech Companies). Tatari has also been recognized as the Best Place to Work by
 
 Inc. Magazine
 
 . Backed by an executive team of former founders and senior leaders from companies including Shazam, TrueCar, AdapTV, LiveRail, Amazon, Google, Meta, Microsoft, and Yahoo, Tatari continues to scale rapidly as TV advertising enters its next major era.
 
 
 We&rsquo;re looking for a full-time data scientist intern to join us for the summer of 2026. In this role, you will work in a fast-paced environment with diverse data sets and technologies to build production quality, scalable and automated solutions for a diverse set of challenging data problems. Under the guidance of our Senior Data Scientists, you will have the freedom to utilize the latest technologies to solve challenging problems, create innovative applications from the ground up, and understand exactly what it takes to create a reliable online experience for our customers. This role collaborates closely with our technology teams to provide profound insights from your analysis, and optimize our business, with the ultimate goal of helping Tatari redefine the TV ad buying experience.
 
 
 
 What You'll Do:
 
 
 
 <ul>
 <li>
 Explore and build data science solutions (e.g., advanced analytics, algorithms, or models) to support measurement or optimization of TV advertising performance
 </li>
 <li>
 Analyze large datasets to generate insights that improve product features or drive better outcomes for clients
 </li>
 <li>
 Collaborate with Data Science and cross-functional partners (Engineering, Product, and Client Services) to gather requirements, review results, and deliver on project goals
 </li>
 <li>
 Present findings and recommendations clearly to technical and non-technical stakeholders across the company
 
 
 </li>
 </ul>
 
 Qualifications:
 
 
 
 <ul>
 <li>
 Currently enrolled in a graduate program in any of the named fields (Data Science, Statistics, Computer Science, Mathematics, Physics, or other STEM field)
 </li>
 <li>
 You are eligible to work full-time from late-May through early August.
 </li>
 <li>
 You have not yet graduated, and you will be continuing with school after this internship
 </li>
 <li>
 Strong interest in designing, and developing analytics and predictive modeling
 </li>
 <li>
 Experience with or interest in Data Manipulation Software (e.g., SQL, Python, Pandas)
 </li>
 <li>
 A quick learner, with the ability to work independently in a fast-paced environment; adaptability and a strong self-teaching ethic are highly valued
 </li>
 <li>
 Strong strategic thinking capabilities
 </li>
 <li>
 Interest to learn to manipulate, analyze, and interpret large amounts of data and to organize findings and translate into actionable insights using original or innovative techniques
 </li>
 <li>
 Knowledge of statistics and probability, particularly their application in systems analysis and operations research
 </li>
 <li>
 Nice to have: experience building and optimizing machine learning models
 </li>
 <li>
 Nice to have: experience with collaborate software engineering use git/GitHub
 
 
 </li>
 </ul>
 
 Benefits + Perks:
 
 
 
 <ul>
 <li>
 Competitive salary ($35.00 - $45.00 hourly)
 </li>
 <li>
 You can pick where you want to be for the summer: San Francisco, Los Angeles, and New York City!
 </li>
 <li>
 Hybrid schedule: 2-3 days per week
 </li>
 <li>
 Snacks, drinks, and catered lunches at the office
 </li>
 <li>
 Team building events
 
 
 </li>
 </ul>
 At Tatari, we believe in the importance of cultivating teams with diverse backgrounds and offering equal opportunities to all. We strive to create a welcoming, inclusive environment where every team member feels valued and diversity is celebrated.
 
 
</div>

Data Science, Intern

Braze

<div>
 At Braze, we have found our people. We&rsquo;re a genuinely approachable, exceptionally kind, and intensely passionate crew.
 
 
 We seek to ignite that passion by setting high standards, championing teamwork, and creating work-life harmony as we collectively navigate rapid growth on a global scale while striving for greater equity and opportunity &ndash; inside and outside our organization.
 
 
 To flourish here, you must be prepared to set a high bar for yourself and those around you. There is always a way to contribute: Acting with autonomy, having accountability and being open to new perspectives are essential to our continued success.
 
 
 Our deep curiosity to learn and our eagerness to share diverse passions with others gives us balance and injects a one-of-a-kind vibrancy into our culture.
 
 
 If you are driven to solve exhilarating challenges and have a bias toward action in the face of change, you will be empowered to make a real impact here, with a sharp and passionate team at your back. If Braze sounds like a place where you can thrive, we can&rsquo;t wait to meet you.
 
 
 
 What You'll Do
 
 
 
 As our customer base continues to grow with the excitement around BrazeAI, we&rsquo;re expanding our team! Join our Forward-Deployed Data Scientist group of creative technical experts who partner with customers to ensure their success. In this role, you will:
 
 
 <ul>
 <li>
 Collaborate with customer Analytics/BI teams and Braze colleagues on implementations, including use case definition, data integration, pipeline setup, and ML model configuration
 </li>
 <li>
 Extend product capabilities by improving architecture and developing reusable data pipelines, APIs, and components
 </li>
 <li>
 Work closely with the RL pipeline development team to refine and advance our reinforcement learning (self-learning) algorithms
 </li>
 <li>
 Contribute to shaping BrazeAI product strategy and roadmap through customer-facing insights and technical expertise
 </li>
 <li>
 Provide ongoing technical expertise to ensure successful adoption, measurable outcomes, and long-term customer success
 
 
 </li>
 </ul>
 
 Who You Are
 
 
 
 <ul>
 <li>
 Education: Bachelor&rsquo;s degree in Computer Science, Data Science, Mathematics, Engineering, or a related field required; Master&rsquo;s or PhD in a relevant technical discipline preferred
 </li>
 <li>
 Experience: 3&ndash;5+ years of hands-on experience as a Data Scientist, Machine Learning Engineer, or similar role working with large-scale data and production environments. Experience in customer-facing or consulting roles is strongly preferred
 </li>
 <li>
 Strong technical expertise: Proficient in Python (Pandas) and core ML libraries (TensorFlow, Keras, scikit-learn, CatBoost, XGBoost). Skilled in SQL for querying/manipulating datasets, with experience in machine learning pipelines and model deployment
 </li>
 <li>
 Engineering best practices: You write well-structured, modular, documented code; follow strong development practices (Git, CI/CD, testing frameworks, type-hinting, code reviews); and can build scalable, maintainable solutions
 </li>
 <li>
 Nice-to-have skills: Experience with DevOps tools (Airflow, Kubernetes, Terraform, GCP), data integration/ETL and pipeline optimization, or reinforcement learning algorithms
 </li>
 <li>
 Customer collaborator: Comfortable working directly with clients and cross-functional teams, aligning stakeholders, and translating technical concepts into clear business value
 </li>
 <li>
 Entrepreneurial problem-solver: You identify opportunities and risks early, troubleshoot obstacles, and drive creative solutions
 </li>
 <li>
 Continuous learner: You stay current with industry trends, explore new tools/technologies, and thrive in environments that push you to grow
 </li>
 <li>
 Clear communicator: Able to explain complex technical ideas persuasively to both technical and non-technical audiences
 
 
 </li>
 </ul>
 For candidates based in the United States, the pay range for this position at the start of employment is expected to be between $124,200 - $148,500/year with an expected On Target Earnings (OTE) between $138,000 - $165,000/year (including bonus or commission). Your exact offer may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. In addition to cash compensation, Braze offers full- and part- time employees a comprehensive Total Rewards package that includes equity grants of restricted stock (RSUs) so that all Braze employees own a piece of our company.
 
 
 
 What We Offer
 
 
 
 Braze benefits vary by location, and we encourage you to review our specific benefits offerings for each country here . More details on benefits plans will be provided if you receive an offer of employment.
 
 
 
 Benefits
 
 
 
 From offering comprehensive benefits to fostering hybrid ways of working, we&rsquo;ve got you covered so you can prioritize work-life harmony. Braze offers benefits such as:
 
 
 <ul>
 <li>
 Competitive compensation that may include equity
 </li>
 <li>
 Retirement and Employee Stock Purchase Plans
 </li>
 <li>
 Flexible paid time off
 </li>
 <li>
 Comprehensive benefit plans covering medical, dental, vision, life, and disability
 </li>
 <li>
 Family services that include fertility benefits and equal paid parental leave
 </li>
 <li>
 Professional development supported by formal career pathing, learning platforms, and a yearly learning stipend
 </li>
 <li>
 A curated in-office employee experience, designed to foster community, team connections, and innovation
 </li>
 <li>
 Opportunities to give back to your community, including an annual company-wide Volunteer Week and donation matching
 </li>
 <li>
 Employee Resource Groups that provide supportive communities within Braze
 </li>
 <li>
 Collaborative, transparent, and fun culture recognized as a Great Place to Work&reg;
 
 
 </li>
 </ul>
 
 About Braze
 
 
 
 Braze is the leading customer engagement platform that empowers brands to Be Absolutely Engaging.&trade; Braze helps brands deliver great customer experiences that drive value both for consumers and for their businesses. Built on a foundation of composable intelligence, BrazeAI&trade; allows marketers to combine and activate AI agents, models, and features at every touchpoint throughout the Braze Customer Engagement Platform for smarter, faster, and more meaningful customer engagement. From cross-channel messaging and journey orchestration to Al-powered decisioning and optimization, Braze enables companies to turn action into interaction through autonomous, 1:1 personalized experiences.
 
 
 The company has repeatedly been recognized as a Leader in marketing technology by industry analysts, and was voted a G2 &ldquo;Best of Marketing and Digital Advertising Software Product&rdquo; in 2025.
 
 
 Braze was also named a 2025 Best Companies To Work For by U.S. News &amp; World Report, a 2025 America&rsquo;s Greatest Companies by Newsweek, and a 2025 Fortune Best Workplace in Technology&trade; by Great Place To Work&reg;, among other accolades. Braze is also proudly certified as a Great Place to Work&reg; in the U.S., the UK, Australia, and Singapore.
 
 
 The company is headquartered in New York with offices in Austin, Berlin, Bucharest, Chicago, Dubai, Jakarta, London, Paris, San Francisco, S&atilde;o Paulo, Singapore, Seoul, Sydney and Tokyo.
 
 
 
 BRAZE IS AN EQUAL OPPORTUNITY EMPLOYER
 
 
 
 At Braze, we strive to create equitable growth and opportunities inside and outside the organization.
 
 
 Building meaningful connections is at the heart of everything we do, and that includes our recruiting practices. We're committed to offering all candidates a fair, accessible, and inclusive experience &ndash; regardless of age, color, disability, gender identity, marital status, maternity, national origin, pregnancy, race, religion, sex, sexual orientation, or status as a protected veteran. When applying and interviewing with Braze, we want you to feel comfortable showcasing what makes you you .
 
 
 We know that sometimes different circumstances can lead talented people to hesitate to apply for a role unless they meet 100% of the criteria. If this sounds familiar, we encourage you to apply, as we&rsquo;d love to meet you.
 
 
 Please see our Candidate Privacy Policy for more information on how Braze processes your personal information during the recruitment process and, if applicable based on your location, how you can exercise any privacy rights.
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Forward-Deployed Data Scientist

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