Data Scientist

Role Overview

Design and deploy advanced statistical models and machine learning algorithms that power product personalization and prediction engines. You’ll translate product questions into rigorous mathematical models, build scalable analytical frameworks, and collaborate closely with data engineering and software engineering teams to bring models into production.

Responsibilities

• Build and deploy production-grade predictive models and statistical analyses using Python, pandas, and scikit-learn
• Design, execute, and analyze A/B tests and multivariate experiments to validate product features and algorithmic changes
• Create robust data pipelines and feature engineering workflows in SQL and PySpark for training and evaluation
• Convert ambiguous business problems into structured analytical frameworks and communicate results to non-technical stakeholders
• Implement model monitoring for performance degradation, data drift, and anomalies in production
• Partner with software engineers to integrate offline-trained models into high-throughput, low-latency microservices

Requirements

• 3–6 years experience as a Data Scientist or Applied Statistician in a product-focused technology environment
• Strong proficiency in Python and SQL with deep understanding of:
  • statistical modeling
  • hypothesis testing
  • regression techniques
• Hands-on experience with statistical experimental design, power analysis, and causal inference
• Experience with large-scale datasets using distributed computing (e.g., Spark, Databricks, or Snowflake)
• Master’s or PhD in Statistics, Computer Science, Applied Mathematics, Economics, or a related quantitative field

Bonus / Nice to Have

• Modern data stack tools such as dbt and Airflow
• Deploying models via Docker and Kubernetes