Data Scientist

Role Overview

As a Data Scientist, you’ll develop and scale predictive models and statistical frameworks that power core business decisions and product features. The work involves transforming complex, unstructured data into high-fidelity models that run reliably in production.

Responsibilities

• Develop, evaluate, and deploy predictive models, including:
  • Regression, classification, and time-series forecasting
• Design and execute rigorous experimentation methods, including A/B testing and multi-armed bandit approaches
• Build and maintain feature engineering and ETL pipelines using SQL, Python, and PySpark on large-scale distributed datasets
• Analyze large datasets to produce actionable insights, and communicate results with cross-functional stakeholders via statistical findings and performance metrics
• Implement automated monitoring for model drift, data quality anomalies, and production performance degradation
• Collaborate with ML engineers to containerize models and integrate into low-latency production APIs

Requirements

• 3–6 years of professional Data Scientist experience, including production deployment of predictive models
• Strong Python and SQL skills; deep familiarity with:
  • pandas, scikit-learn, XGBoost, statsmodels
• Solid theoretical foundation in statistics, hypothesis testing, experimental design, and causal inference
• Experience with cloud data platforms and distributed computing, specifically:
  • AWS (S3, Redshift)
  • Snowflake or Databricks
• Master’s or PhD in a highly quantitative field (e.g., Statistics, CS, Applied Math, Economics)

Bonus

• Experience with Deep Learning frameworks (PyTorch or TensorFlow)
• Experience deploying models using Docker and Kubernetes