Data Scientist

About The Role

The role sits at the intersection of applied statistics, machine learning, and product decision-making - turning messy, large-scale data into models and analyses that drive core business decisions.

As a mid-level Data Scientist, the work spans exploratory analysis, causal inference, and production ML. The role will own projects end-to-end: framing the problem, designing experiments, building models, and shipping measurable impact with engineering and product partners.

Key Responsibilities

• Design and analyze A/B tests and quasi-experimental studies, including power analysis, variant design, and statistical significance frameworks
• Build, validate, and iterate on predictive models (classification, forecasting, churn, propensity) using Python, scikit-learn, and gradient boosting libraries (XGBoost, LightGBM)
• Own the transition of models from prototype to production, partnering with ML engineers on deployment, monitoring, and retraining pipelines
• Develop SQL-based data pipelines and feature definitions, working closely with analytics engineers on data quality and metric consistency
• Translate business questions into rigorous analytical frameworks and communicate findings through clear visualizations and stakeholder-ready narratives (Tableau, Plotly, Streamlit)
• Conduct deep-dive analyses to diagnose product and revenue anomalies, identifying root causes and quantifying opportunities
• Contribute to team standards for model documentation, reproducibility, and experiment tracking (MLflow, Weights & Biases)

What We Are Looking For

• 3–6 years of experience in data science, applied statistics, or machine learning, with a track record of shipped models or high-impact analyses
• Expert-level SQL and strong Python; comfort with pandas, NumPy, and at least one workflow orchestration tool (Airflow, dbt)
• Hands-on experience designing and analyzing controlled experiments, including causal inference techniques (diff-in-diff, synthetic control, matching)
• Solid grounding in statistics and ML fundamentals: regularization, cross-validation, feature leakage, bias-variance tradeoff
• Experience communicating quantitative findings to non-technical stakeholders and influencing product or business decisions
• MS or PhD in a quantitative field (Statistics, Computer Science, Economics, Engineering) or equivalent industry experience
• Bonus: Experience with Snowflake/BigQuery at scale, causal ML libraries (EconML, DoWhy), LLM-assisted analytics workflows, or prior work in marketplace, fintech, or SaaS domains