Senior Data Scientist

About the role

Button’s mission is to build a better internet, fueled by commerce. We work with some of the largest and most interesting businesses in the world to connect consumers with what they want at the tap of a button. We are a deeply data-driven company where machine learning powers recommendation, ranking, routing, monetization, experimentation, and product strategy.

Responsibilities

• Design, test, and iterate on machine learning models that power recommendation, ranking, routing, and monetization
• Own A/B testing strategy and causal measurement for product features and partner performance
• Partner with engineers to productionize models and integrate them into live systems
• Build analyses, dashboards, and tools that inform product strategy and customer outcomes
• Define metrics and success criteria for new initiatives
• Deep dive into product and user data to derive actionable insights and size opportunities to improve products, strategy and operations, influencing roadmaps through insights and recommendations
• Help establish foundational data practices and help scale our analytics infrastructure to support rapid iteration and decision-making as our products grow leveraging our existing technical stack
• Raise the bar for statistical rigor and experimentation across the company

Requirements

• Strong product instincts and comfort operating in ambiguity
• Ability to independently scope problems and drive solutions
• Clear communicator who can explain technical tradeoffs to non-technical partners
• Highest level of written communication and presentation skills
• A track record of translating complex data into clear, actionable insights for both technical and business stakeholders
• Ability to thrive in ambiguous, fast-moving environments where you must create clarity, drive forward progress, and pivot when necessary
• Strong grasp of model lifecycle management (real-time performance tracking, data drift monitoring, retrain intervals)
• Bias toward shipping and measurable impact
• Collaborative mindset across Product, Engineering, and GTM teams
• 5+ years applying statistics or machine learning in production environments
• Deep understanding of experimentation, causal inference, and Bayesian or frequentist methods
• 3+ years of experience working closely with Product or Engineering teams on API or developer-facing products, with demonstrated impact on product roadmap and strategy
• Strong Python skills
• Strong SQL proficiency and proven track record working with large scale datasets
• Experience with AWS, distributed systems, and high volume data pipelines
• Experience deploying and maintaining models in production
• Solid foundation in linear algebra, optimization, and model evaluation
• Experience with AI/ML operations & tooling: understanding of API rate limiting, inference workload patterns, accelerator management
• Experience in marketplaces, ads tech, retail media, or growth optimization
• Strong instincts for what drives product adoption, engagement, and retention
• Familiarity with both B2C and B2B marketing analytics, and a holistic view of how different product verticals support one another
• Experience with ranking, pricing, or allocation problems
• Experience with feature stores, model monitoring, or ML platforms
• Experience with MLFlow (or similar tools), Sagemaker, Vector Databases (Pinecone, Faiss, Amazon MemoryDB), SparkML, Docker, Experimentation Platforms (Statsig, Eppo by Datadog)

Qualifications

• 5+ years applying statistics or machine learning in production environments
• Deep understanding of experimentation, causal inference, and Bayesian or frequentist methods
• 3+ years of experience working closely with Product or Engineering teams on API or developer-facing products, with demonstrated impact on product roadmap and strategy
• Strong Python skills
• Strong SQL proficiency and proven track record working with large scale datasets
• Experience with AWS, distributed systems, and high volume data pipelines
• Experience deploying and maintaining models in production
• Solid foundation in linear algebra, optimization, and model evaluation
• Experience with AI/ML operations & tooling: understanding of API rate limiting, inference workload patterns, accelerator management
• Experience in marketplaces, ads tech, retail media, or growth optimization
• Strong instincts for what drives product adoption, engagement, and retention
• Familiarity with both B2C and B2B marketing analytics, and a holistic view of how different product verticals support one another
• Experience with ranking, pricing, or allocation problems
• Experience with feature stores, model monitoring, or ML platforms
• Experience with MLFlow (or similar tools), Sagemaker, Vector Databases (Pinecone, Faiss, Amazon MemoryDB), SparkML, Docker, Experimentation Platforms (Statsig, Eppo by Datadog)

Skills

• Strong product instincts and comfort operating in ambiguity
• Ability to independently scope problems and drive solutions
• Clear communicator who can explain technical tradeoffs to non-technical partners
• Highest level of written communication and presentation skills
• A track record of translating complex data into clear, actionable insights for both technical and business stakeholders
• Ability to thrive in ambiguous, fast-moving environments where you must create clarity, drive forward progress, and pivot when necessary
• Strong grasp of model lifecycle management (real-time performance tracking, data drift monitoring, retrain intervals)
• Bias toward shipping and measurable impact
• Collaborative mindset across Product, Engineering, and GTM teams
• 5+ years applying statistics or machine learning in production environments
• Deep understanding of experimentation, causal inference, and Bayesian or frequentist methods
• 3+ years of experience working closely with Product or Engineering teams on API or developer-facing products, with demonstrated impact on product roadmap and strategy
• Strong Python skills
• Strong SQL proficiency and proven track record working with large scale datasets
• Experience with AWS, distributed systems, and high volume data pipelines
• Experience deploying and maintaining models in production
• Solid foundation in linear algebra, optimization, and model evaluation
• Experience with AI/ML operations & tooling: understanding of API rate limiting, inference workload patterns, accelerator management
• Experience in marketplaces, ads tech, retail media, or growth optimization
• Strong instincts for what drives product adoption, engagement, and retention
• Familiarity with both B2C and B2B marketing analytics, and a holistic view of how different product verticals support one another
• Experience with ranking, pricing, or allocation problems
• Experience with feature stores, model monitoring, or ML platforms
• Experience with MLFlow (or similar tools), Sagemaker, Vector Databases (Pinecone, Faiss, Amazon MemoryDB), SparkML, Docker, Experimentation Platforms (Statsig, Eppo by Datadog)

Benefits

• RemotePlus workplace blending “work from anywhere” with in-person collaboration
• Hub workspace in New York City
• Distributed team across the United States and beyond
• Competitive base salary with on-target earnings range of $160,000 - $208,000 (offered salary is based on a number of factors including skills and experience relative to the job description listed above)
• 401(k) plan with automatic 3% contribution
• Unlimited time off (including birthdays off) and periodic Mental Health Weeks
• Employee Assistance Program
• Health, vision, and dental insurance plans with 100% coverage for employees and 75% for dependents
• Complimentary memberships to One Medical and a monthly stipend for mobile phone/internet
• An annual lifestyle stipend
• “All Access” memberships to WeWork and regular “coworking days” and social events

Pay

Base salary competitive with what is offered by similar companies in major US markets.

Schedule

Remote (United States)