Description

You will apply applied data science and AI-native workflows to deliver production-grade analytical assets in complex client environments.

This role is hybrid.

Responsibilities

• Build reusable Python and SQL workflows that transform messy enterprise data into reliable analytical solutions.
• Operate as a modern technical builder using Git, containers, and terminal tools for reproducible environments.
• Leverage LLMs and coding agents for exploration, code generation, and debugging while maintaining strict statistical judgment.
• Troubleshoot failed jobs, broken queries, and data-quality issues independently without immediate engineering support.
• Collaborate with data and ML engineers on batch pipelines, feature generation, and model deployment handoffs.

Required Skills

• PhD, Master’s, or Bachelor’s degree with clear evidence of applied data science delivery.
• Fluent in Python and SQL for data extraction, transformation, and analysis.
• Experience with cloud data warehouses or lakehouses: Snowflake, Databricks, BigQuery, Redshift, or Spark.
• Proficient in Git workflows including branches, pull requests, and code review.
• Ability to detect plausible but incorrect AI-generated output and validate statistical assumptions.
• Understanding of production constraints: CI/CD, model versioning, data contracts, and monitoring.
• Strong background in statistics, computer science, operations research, or applied mathematics.

Preferred Skills

• Experience with forecasting, experimentation, and optimization techniques.
• Ability to create model cards, validation notes, and handoff documentation.
• 10+ years of professional experience in data science or applied analytics.

Key Skills

Education

Any Graduate