Data Scientist

About The Role

The role is for a hands-on data scientist who turns messy, real-world data into models that ship to production and drive measurable business decisions - not dashboards that sit unread.

You will work with ML engineers and data engineers across the full modeling lifecycle: problem framing, experimentation, statistical validation, and deployment, in a stack built on Python, SQL, Spark, and modern cloud tooling.

Key Responsibilities

• Frame ambiguous business problems as testable ML and statistical questions, and communicate tradeoffs to technical and non-technical stakeholders
• Design and run controlled experiments (A/B tests, quasi-experimental designs), computing sample sizes, guardrail metrics, and causal estimates with rigor
• Build, train, and validate predictive models - gradient boosting, logistic regression, time-series forecasting, and NLP classifiers - using scikit-learn, XGBoost, and PyTorch
• Develop feature pipelines in Python and PySpark, partnering with data engineers to ensure reliability from raw data through training and inference
• Partner with ML engineers to productionize models, including feature stores, batch/online scoring, and monitoring for drift and performance decay
• Communicate results through clear analyses, technical write-ups, and visualizations using SQL, notebooks, and BI tooling (Looker or Tableau)

What We Are Looking For

• 3–6 years of experience in data science or applied machine learning, with at least one model taken from prototype to production
• Expert-level SQL and strong Python for data manipulation and modeling (pandas, NumPy, scikit-learn)
• Hands-on experience with A/B testing and causal inference techniques; you can explain when an uplift model or difference-in-differences is the right tool
• Experience processing large datasets with Spark, Databricks, or equivalent distributed tooling
• Master's degree in statistics, computer science, economics, or a related quantitative field (or equivalent practical experience)
• Strong written and verbal communication - able to defend methodology and simplify findings for executive audiences
• Bonus: experience with dbt, Airflow, MLflow, or experimentation platforms (e.g., in-house A/B tooling); exposure to LLM-based analytics or retrieval systems