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about 2 months ago
Requirements and responsibilities
About TunnlTunnl is building a future where artificial intelligence enables organizations to connect meaningfully with the people who matter most. We help organizations conduct research at scale, define the right audiences, surface real-time insights, identify optimal communication channels, and measure changing attitudes over time.Tunnl serves brands, agencies, and advocacy groups alike—organizations navigating complex communications, reputational, and regulatory landscapes. These teams need smarter, faster ways to make audience-informed decisions that stand up to scrutiny and resonate across stakeholder groups. Whether you're building a brand, shaping public opinion, managing risk, or launching a new initiative, Tunnl empowers you to move from insight to impact with clarity & confidence.The RoleAs a Data Scientist II at Tunnl, you will contribute to the development and improvement of machine learning systems that power audience intelligence, targeting, and measurement across television and digital channels. You'll work within established ML patterns on well-scoped problems, with increasing independence as you grow into the role. You'll be embedded in a collaborative data science team and work closely with senior data scientists who provide technical guidance and code review.This role sits at the intersection of data science and AdTech—your work will directly affect audience targeting quality and measurement accuracy for thousands of advertisers.In your first 6 months, you'll be successful if you: ship improvements to at least one production model with guidance from the team; can independently debug pipeline issues and model quality problems; contribute substantially to building a data science idea into a working proof of concept; and communicate clearly about your work in team reviews and cross-functional meetings.What You'll DoBuild and improve machine learning models for audience targeting, lookalike generation, and individual propensity scoringContribute across the ML lifecycle—from exploratory analysis and experimentation through production deployment and monitoring, within established team patterns and with guidance from senior data scientistsDesign and analyze experiments (A/B tests, holdout studies) to evaluate model performance and measure business impactTranslate analytical findings into clear, concise communications for technical and non-technical audiences including product and customer success teamsEngineer features from demographic, behavioral, and identity data—including handling missing values, encoding strategies, and data quality validationWrite well-tested, documented, and maintainable code; participate in code reviews and contribute to improving team coding standardsPartner closely with data engineers to ensure feature pipelines are reliable and well-documented, and work with product managers to align model outputs with business requirementsWork with distributed computing (Spark/Databricks) and cloud data platforms (AWS, Snowflake) to build and contribute to production ML pipelinesWhat We're Looking ForRequired:2–4 years of experience in Data Science or Machine Learning, with a track record of delivering end-to-end ML projects, or demonstrated equivalent experience through strong project work or a portfolioSolid grounding in statistics and probability; experience designing or analyzing experiments such as A/B tests or holdout studiesStrong proficiency in Python and SQLExperience querying and processing large datasets using Spark or Databricks in a cloud environment (AWS preferred); comfort running and monitoring scheduled jobsExperience contributing to production ML pipelines including batch model training, scoring, and evaluationFamiliarity with supervised classification or segmentation modeling applied to real-world datasetsFamiliarity with software engineering best practices: git, automated tests, and code review workflowsStrong communication skills with the ability to collaborate effectively across technical and non-technical teamsPreferred:B.S. in computer science, statistics, data science, or a quantitative field; M.S. a plus but not requiredAdTech or audience intelligence experience; familiarity with audience modeling, lookalike systems, or ML-driven targetingExposure to survey research methodology, including sample design, survey weighting, and bias/representativeness considerationsSome exposure to vector similarity and approximate nearest neighbor systems (FAISS or equivalent)Experience with scikit-learn and XGBoost for supervised classification; familiarity with PyTorch is a plusExperience with data visualization tools or libraries (Matplotlib, Seaborn, Tableau, or equivalent) or comfort presenting analytical findings to non-technical audiencesExposure to or interest in GenAI tooling and LLM integrationAwareness of or interest in self-supervised or representation learning approachesWhy You Should ApplyJoin a team driven by curiosity, teamwork, integrity, and a shared passion for solving big challenges.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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