Senior Data Scientist
The role delivers data science projects spanning predictive modeling, experimentation, causal analysis, and AI-powered workflows to improve product, marketing, and partner outcomes. It requires 4–7+ years of quantitative experience, strong Python and SQL skills, and the ability to communicate insights across technical and business teams.
About the job
Responsibilities
- Deliver analytical projects that influence product decisions, marketing campaigns, and business strategy from problem definition through deployment and monitoring.
- Build segmentation frameworks and predictive models, including churn, lifetime value, and propensity models, to drive targeting, personalization, and lifecycle optimization across partner programs.
- Support A/B testing and experimentation by designing, analyzing, and interpreting experiments with Product, Marketing, and Commercial teams.
- Apply statistical inference, causal analysis, and experimentation design to improve LTV/CAC ratios and accelerate feedback loops on business performance.
- Design and build agentic workflows and AI-powered systems that explore data, generate hypotheses, monitor business metrics, and operationalize decisions.
- Translate complex data into clear narratives for leadership and partner stakeholders.
- Contribute to team excellence through code reviews, knowledge sharing, and process improvements.
Requirements
- 4 to 7+ years of experience in data science, analytics, or a related quantitative field, ideally in a high-growth startup or fintech company.
- Degree in statistics, engineering, science, finance, or a similar relevant field; a graduate degree is a plus.
- Strong Python and SQL skills, including transforming raw data, building custom datasets, and shipping models to production.
- Strong foundation in statistical inference, experimentation design, and causal analysis.
- Experience using LLMs and AI tools such as Claude, Copilot, or Cursor in analytical and automation workflows.
- Ability to communicate complex findings to technical and non-technical audiences, including senior leadership and external partners.
- Full-stack problem-solving orientation and comfort working with messy data, testing assumptions, and validating results.
- Experience owning projects end to end in a fast-moving startup environment and collaborating cross-functionally.
Nice-to-Haves
- Experience in credit, lending, or card products.
- Experience with experimentation infrastructure or ML infrastructure.
- Exposure to lifecycle marketing, prescreen modeling, or customer segmentation at scale.
- Background in time series analysis, forecasting, optimization, or simulation.
- Familiarity with dashboarding tools such as Sigma or Looker.
Technology Stack
- Python
- SQL
- Snowflake
- dbt
- Sigma
- AWS
Compensation and Benefits
- Competitive compensation and equity packages.
- Flexible paid time off.
- Fully covered healthcare, including dependent coverage.
- Access to One Medical and an FSA option.
- 20 weeks of paid parental leave for the primary caregiver and 8 weeks for all new parents.
- Work computers of the employee's choice.
Skills
Python, SQL, Statistical Inference, A/B Testing, Causal Analysis, Machine Learning, LLMs, Snowflake, dbt, Sigma, AWS, Time Series Analysis, Forecasting, Customer Segmentation
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