Data Scientist

Role Overview

The Data Scientist will lead end-to-end data science initiatives, building and deploying advanced statistical and machine learning models that turn complex business problems into actionable, predictive insights. The role focuses on improving customer retention, pricing strategies, and user personalization through production-grade modeling and experimentation.

Responsibilities

• Design, train, and validate predictive models to optimize key business KPIs using Python and ML/deep learning frameworks.
• Build and maintain feature engineering pipelines with SQL, Spark, and pandas for high-fidelity training and inference data.
• Deploy models in production by partnering with data platform engineers to ship models as microservices using Docker and Kubernetes or cloud-native services (e.g., AWS SageMaker).
• Run rigorous experimentation, including A/B testing frameworks and multi-armed bandit approaches to measure real-world impact.
• Implement monitoring to detect model drift, data quality issues, and concept decay.
• Communicate results by translating analyses into clear visualizations and strategic recommendations for cross-functional stakeholders.

Requirements

• 3–6 years of experience as a Data Scientist or Applied Scientist, including shipping ML models to production.
• Strong Python and SQL skills, with deep knowledge of statistical modeling, experiment design, and hypothesis testing.
• Experience with cloud infrastructure (AWS or GCP) and Docker.
• Solid software engineering practices: Git, unit testing, and CI/CD pipelines.
• Bachelor’s or Master’s degree in a quantitative field.

Bonus / Nice to Have

• Experience with NLP
• PySpark
• Workflow orchestration tools such as Apache Airflow