Senior Data Scientist

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What You’ll Be Doing:

• Architect our data science function from the ground up, setting the strategic vision to drive data-informed decision making across all parts of Relay
• Elevate our experimentation capabilities by designing and implementing robust, statistically sound processes and tooling
• Collaborate with our Data Engineering and Growth Engineering teams to introduce advanced methodologies beyond simple A/B testing, driving precise, data-informed product decisions and growth strategies
• Be at the forefront of innovation as you help us prepare for a future where predictive, ML-driven decision-making capabilities are diffused throughout the entire organization. Your work will set the foundation for our long-term technological advancement
• Drive real-world results by balancing sophistication with practicality. You'll have the autonomy to think long-term, partnering with Data Analysts and Engineers to bring solutions out of proof-of-concept and into production
• Influence product direction through close collaboration with Product, Engineering, and Marketing teams. Your data insights will directly shape our product enhancements and strategies, allowing you to see the tangible outcomes of your work
• Make your mark on our company's trajectory by providing strategic, data-driven recommendations to leadership. Your insights will directly influence business growth and operational efficiency, giving you a seat at the table in shaping our future
• Help cultivate a data-driven culture across the company. You'll have the opportunity to establish best practices, mentor team members, and advocate for data-informed decision making across the organization, leaving a lasting impact on how we operate

What You’ll Bring:

• 6+ years of experience in Data Science or a related field
• Master's degree or PhD in Computer Science, Statistics, Mathematics, or a related quantitative field. Exceptional candidates with a Bachelor's degree and significant relevant experience may also be considered 
• Expert-level SQL proficiency, including advanced query optimization, complex aggregations, and a deep understanding of data warehousing concepts and security best practices
• Extensive experience designing, implementing, and analyzing complex experimentation frameworks, including A/B/n tests, multi-armed bandits, and quasi-experimental methods 
• Demonstrated ability to apply advanced statistical techniques to derive actionable insights from experimental data and guide product decisions
• Proven ability to drive business decisions from complex data into clear, actionable visualizations and clear insights 
• Experience working with modern data stack technologies, including cloud-based data warehouses (e.g. Bigquery, Snowflake, Redshift, Clickhouse, etc.) and business intelligence platforms (e.g. Looker, Metabase, Periscope, Mode, etc.)
• Mastery of fundamental statistical and machine learning principles, with the discernment to select and apply appropriate methodologies for each unique problem space. Experience spans from rigorous hypothesis testing and causal inference to advanced predictive modeling, always with a focus on interpretability and practical impact
• Advanced programming skills in Python and extensive experience with data science libraries (e.g., pandas, scikit-learn, PyTorch). While we primarily use Python, strong R skills are also valuable. Ability to quickly adapt to our Python-centric environment is essential
• Strong communication skills with the ability to translate complex technical concepts to non-technical stakeholders
• Demonstrated ability to lead projects independently and collaborate effectively with cross-functional teams
• Adaptability to handle change and uncertainty at times; We are a startup after all!

Bonus Points:

• Experience in fintech, banking, or SaaS industries, with deep understanding of key metrics like LTV, CAC, churn, and retention
• Familiarity with MLOps practices and tools for model versioning, monitoring, and deployment
• Knowledge of privacy-preserving machine learning techniques and experience working with sensitive financial data
• Experience with real-time analytics and streaming data processing (e.g., Kafka, Flink, Spark Streaming)
• Background in natural language processing (NLP) for applications like sentiment analysis or document classification
• Contributions to open-source data science projects or peer-reviewed publications in relevant fields
• Experience mentoring junior data scientists and fostering a data-driven culture in fast-growing organizations

Our Commitment To You:

• Competitive salary and meaningful equity: every team member gets a piece of the pie 
• Comprehensive health benefits: we offer full health benefits + an HSA/WSA starting from day 1 so you get the coverage you need
• 4 weeks vacation + end of year holiday shutdown: we take time off to reset and recharge so we come back better for our customers  
• Hybrid work environment: we love collaborating and connecting in office two times a week and offer catered lunches and a snack/beverage program for the days we’re in office. Don’t forget to bring in your furry friends!
• Personal and professional growth: support from leaders who care about your growth and success through regular feedback and coaching. Our goal is to make Relay a step-change career opportunity
• Top-tier equipment: we’re a Mac environment and we’ll make sure you have everything you need to produce your best work 
• Team-first culture: we’re passionate about working collaboratively, bonding through team events, and most importantly having fun

The Interview Process:

• Stage 1: A 30-minute Google Meets video call with a member of the Talent team
• Stage 2: A 45-minute Google Meets video call with our Director of Data and Insights
• Stage 3: A take-home Data Science case study, followed by a 1-hour interview with members of the Data and Product teams mirroring our Technical Design Document (TDD) review process
• Stage 4: A 30-45 minute Google Meets (or in-person) values interview with a non-technical member of the Relay senior leadership team