Data Scientist

Role Overview

Own the end-to-end data science lifecycle—turn complex datasets into predictive models and actionable business intelligence that informs product strategy. Partner with product managers and data engineers to build pipelines, design experiments, and deliver insights with a focus on precision and scalability.

Responsibilities

• Design, train, and validate statistical and machine learning models for forecasting and classification problems
• Build automated ETL pipelines in Python and SQL to ingest, clean, and process large-scale structured and unstructured data
• Design and analyze A/B tests to assess product feature impact, ensuring statistical significance and clear, actionable conclusions
• Deploy models to production and monitor performance to prevent drift and degradation over time
• Communicate technical findings to cross-functional stakeholders using data visualizations and reports

Requirements

• 3–6 years of experience in data science, applied statistics, or quantitative analytics in a production environment
• Advanced proficiency in Python and SQL, including pandas, scikit-learn, and data visualization libraries
• Demonstrated expertise in hypothesis testing, experimental design, and statistical modeling
• Familiarity with cloud data platforms such as Snowflake, BigQuery, or AWS Redshift
• Familiarity with orchestration tools such as Airflow

Nice to Have

• Experience deploying models via Docker/Kubernetes
• Familiarity with LLM architectures
• A Master’s degree in a quantitative field