Tunnl - Staff/ Principal Data Scientist
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
Responsibilities
• 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
Benefits
• A friendly, welcoming, and supportive culture with regular social and team events. • Comprehensive benefits with excellent medical, vision, and dental coverage. • Health Savings Account (HSA) and Flexible Spending Account (FSA) options. • Employer-paid life insurance & short-term & long-term disability, with other voluntary additional coverage available (accident, critical illness, hospital indemnity). • 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.
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