Data Scientist

Digital products play a central role in how we create value for customers, support the teams who serve them, and shape the consumer experience. Our product organization brings together small, empowered teams that move with clarity, speed, and purpose, enabling digital to be a meaningful source of advantage across our operating unit.

Our work touches the experiences that keep the business running, including customer journeys, service delivery, sales workflows, and the systems that connect them. Elevating our data and experimentation capability is essential to unlocking new value and guiding the evolution of our products and platforms.

About the Role

Data Scientists in our product organization are embedded in empowered, cross-functional teams. You will work alongside Product Managers, Designers, Engineers, and Analysts to uncover insights, design experiments, and build models that shape how customers, partners, and associates experience our digital products.

This role blends research, modeling, experimentation, and strategy. You will turn raw data into insight, insight into clarity, and clarity into decisions that help teams learn faster, reduce uncertainty, and make meaningful progress. Your work will influence how businesses operate, how issues are resolved, how sales teams engage customers, and how our platforms evolve.

We are looking for someone with curiosity, statistical rigor, creativity, and strong product instinct.

Responsibilities

• Use advanced analytical and statistical methods to understand user behavior, product performance, and the impact of new features
• Design, run, and interpret A/B tests, incrementality experiments, and quasi-experimental analyses
• Define and evolve core product metrics that track engagement, health, value creation, and long-term success
• Build predictive or machine-learning models to personalize experiences, forecast demand, or reveal patterns that unlock new opportunities
• Partner with Product, Design, Engineering, Data, Commercial, and Operations to shape product strategy with rigorous insight
• Work with data engineering to ensure trustworthy instrumentation, scalable pipelines, and high-quality measurement foundations
• Develop reusable frameworks, measurement playbooks, and experimentation standards that elevate analytical maturity across teams
• Communicate insights through clear stories, visualizations, and recommendations that guide priorities and product decisions
• Advocate for thoughtful, ethical, and high-quality use of data across the product organization

• 4+ years of experience applying data science techniques in a product or digital environment
• Education: Bachelor's or Master's degree in Statistics, Computer Science, Data Science, Applied Math, Economics, Engineering, or a related quantitative field
• Strong proficiency with SQL and Python (or R) and experience working with large, complex datasets
• Hands-on experience designing, executing, and analyzing A/B tests and incrementality experiments
• Strong foundation in statistics, causal inference, modeling, and data exploration
• Experience with modern analytics or experimentation tools such as Snowflake, Databricks, Amplitude, Looker, or Optimizely
• Ability to distill complex analyses into clear insights and recommendations
• Proven success influencing decisions in cross-functional environments

• Experience working with multimodal or behavioral datasets
• Experience deploying predictive models into production or working within modern ML environments
• Familiarity with LLMs, generative AI, or agentic systems
• Experience in B2B or multi-stakeholder environments
• Comfort with data visualization tools such as Looker or Tableau

• Analytical rigor: Applies strong statistical and causal methods to break down ambiguous problems and uncover meaningful drivers, patterns, and opportunities.
• Product thinking: Connects modeling and experimentation to real user behavior, value creation, and product strategy; influences direction through clarity and evidence.
• Experimentation and measurement: Designs robust KPIs, constructs reliable measurement frameworks, and leads A/B and quasi-experimental analyses that reduce uncertainty and accelerate learning.
• Modeling expertise: Builds interpretable, scalable models that inform decisions, enable personalization, forecast behavior, or reveal new opportunities.
• Data storytelling: Synthesizes complex analyses into clear narratives and visualizations that influence priorities and align cross-functional teams.
• Collaboration and influence: Works deeply within product teams, elevates analytical standards, and helps teams adopt evidence-based decision-making.
• Bias for impact: Balances rigor with speed; focuses on insights and models that meaningfully shape decisions and outcomes.