Principal Data Scientist

Role Overview

• Design, build, and deploy machine learning solutions for audience targeting, lookalike generation, and individual propensity scoring
• Own the complete ML lifecycle
• from exploratory analysis and experimentation all the way through production deployment and operational monitoring
• Develop and ship production ML systems spanning self-supervised representation learning, vector similarity search, and supervised classifiers
• Leverage distributed computing (Spark/Databricks) and cloud data platforms (AWS, Snowflake) to build and run production ML pipelines at scale
• Ensure model quality through rigorous evaluation practices: from embedding validation and retrieval quality to supervised model calibration and production monitoring
• Engineer features at scale from demographic, behavioral, and identity data — including handling missing values, encoding strategies, and pipeline-level data quality validation
• Contribute ML logic directly into shared production services, working alongside data engineering, software engineering, and product teams

Requirements

• 8+ years of experience in Data Science or Machine Learning, with a proven track record of delivering high-impact end-to-end ML solutions
• Master-level proficiency in Python and SQL
• Strong experience with big data and cloud infrastructure (Spark/Databricks, AWS S3, or equivalents)
• Expertise deploying and maintaining production ML pipelines including batch model training, large-scale scoring runs, async job orchestration, evaluation and monitoring
• Strong experience in audience intelligence or AdTech, with deep knowledge of audience modeling, lookalike/similarity systems, and ML-driven targeting at scale
• Hands-on experience with vector similarity and approximate nearest neighbor systems (FAISS or equivalent) — including index
• construction, search quality tradeoffs, and production embedding serving
• Experience with software engineering best practices: git, automated tests, CI/CD, and code deployment
• Exceptional communication skills with the ability to influence technical and non-technical stakeholders
• M.S. or PhD in computer science, applied mathematics, statistics, data science, or a quantitative field with strong ML/modeling foundations
• Experience with GenAI tooling and LLM integration — particularly building structured recommendation or explanation layers grounded in ML model outputs
• Experience with self-supervised or representation learning approaches, particularly Transformer-based architectures for structured or semi-structured data
• Production experience with PyTorch for deep learning and embedding models, scikit-learn and XGBoost for supervised classification pipelines.

Tech Stack

• AWS
• Cloud
• Python
• PyTorch
• Scikit-Learn
• Spark
• SQL

Benefits

• Eligible for the Company Bonus Plan (targeting 15% of Base Salary).
• Comprehensive benefits with excellent medical, vision, and dental coverage.
• Health Savings Account (HSA) and Flexible Spending Account (FSA) options.
• Employer-paid life insurance, with voluntary additional coverage available.
• Voluntary short
• and long-term disability, accident, and critical illness insurance.
• Flexible hybrid work policy.
• Flexible unlimited paid vacation plus 80 hours of paid sick leave.
• 10 paid company holidays per year plus the week between Christmas and New Year’s off.
• 401(k) plan with 100% match up to 3%, plus 50% match up to 5% (subject to IRS limits).
• Cell phone reimbursement stipend.
• Monthly parking or commuter stipend for VA-based employees.