Data Scientist

About The Role

The role sits at the center of a data platform that powers real product decisions: forecasting demand, scoring risk, detecting anomalies, and surfacing insights that directly change what the business ships next.

You will work with a small, senior team of ML engineers and data engineers, translating messy, high-volume data into models and analyses that hold up under production scrutiny - not just in a notebook.

Key Responsibilities

• Design, build, and validate predictive models (forecasting, classification, propensity, anomaly detection) using Python, scikit-learn, XGBoost, and PyTorch
• Own exploratory analysis and experimentation: design A/B tests, compute power analyses, and deliver statistically sound conclusions to product and engineering stakeholders
• Build and productionize data pipelines in SQL, PySpark, and Airflow, partnering with data engineering on data quality and lineage
• Develop dashboards and self-serve analytics in Looker, Tableau, or Streamlit that make model outputs actionable across the org
• Collaborate with ML engineers to move models from prototype to production, including feature stores, evaluation harnesses, and drift monitoring
• Communicate findings clearly and honestly - in writing and in stakeholder reviews - including the limitations and tradeoffs of every model you ship

What We Are Looking For

• 3–6 years of hands-on data science experience, including at least one model taken from research to production
• Expert-level SQL and Python; strong pandas/NumPy fluency and comfort with large-scale data (Spark a plus)
• Solid statistical foundation: hypothesis testing, causal inference, experimental design, and time-series methods
• Experience with cloud data stacks (Snowflake, Databricks, BigQuery, or Redshift) and workflow tooling like Airflow or dbt
• MS or PhD in a quantitative field (Statistics, Computer Science, Math, Economics) or equivalent practical experience
• Track record of communicating technical results to non-technical stakeholders and influencing product decisions
• Bonus: experience with LLM applications, causal ML ( uplift modeling, doubly robust estimation ), or MLOps tooling such as MLflow and SageMaker