Principal Data Scientist
Tunnl
full-time
Posted on:
Location Type: Remote
Location: United States
Visit company websiteExplore more
Job Level
About the role
- 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
- Preferred:
- 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
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.
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills & Tools
machine learningPythonSQLbig datacloud infrastructurevector similarityapproximate nearest neighborself-supervised learningdeep learningsupervised classification
Soft Skills
communicationinfluencecollaboration
Certifications
M.S.PhD