Staff/ Principal Data Scientist

About the Role

The role entails collaboration with cross-functional teams, including data engineering, software engineering, and product teams, to develop and deploy production-grade machine learning systems. You will be working with large datasets and complex systems, and will need to be able to communicate technical concepts effectively to both technical and non-technical stakeholders.

Tunnl is a company that values innovation, teamwork, and integrity, and is committed to solving big challenges in the field of audience intelligence. As a member of the team, you will be expected to embody these values and contribute to a culture of curiosity, respect, and open communication.

What You Will Do

• 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 to 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, including embedding validation and retrieval quality, 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
• Collaborate with cross-functional teams to develop and deploy production-grade machine learning systems
• Communicate technical concepts and results effectively to both technical and non-technical stakeholders

What We Are Looking For

• 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)
• Experience with software engineering best practices, including git, automated tests, CI/CD, and code deployment
• Exceptional communication skills, with the ability to influence technical and non-technical stakeholders

Nice to Have

• 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 and Perks

• Eligible for the Company Bonus Plan (targeting 15% of Base Salary)
• Comprehensive benefits, including 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 company match
• Opportunities for professional growth and development in a rapidly growing company
• Access to cutting-edge technologies and tools
• Collaborative and dynamic work environment with a team of experienced professionals

• To stand out as a candidate, make sure to highlight your experience with machine learning pipelines and model evaluation, as well as your ability to communicate technical concepts effectively to non-technical stakeholders.
• When preparing for interviews, review your experience with distributed computing and cloud data platforms, and be prepared to discuss your approach to ensuring model quality and deploying production-grade machine learning systems.
• Be prepared to provide examples of your experience with audience intelligence and AdTech, and to discuss your understanding of the latest trends and technologies in the field.
• When negotiating salary, be sure to research the market rate for similar positions and to highlight your unique qualifications and experience.
• Red flags to watch for in the interview process include a lack of transparency about the company culture or expectations, or a lack of clarity about the role or responsibilities.