Sr. Data Scientist, Forward-Deployed at 6sense in San Francisco, CA

Job description

Our Mission: 6sense's mission is to multiply what matters: growth, retention, and efficiency. We envision a future where companies, teams and people reach their full potential. Our People: People are the heart and soul of 6sense. We serve with passion and purpose. We live by our Being 6sense values of Win as One Team, Stay Curious, Do The Right Thing, Own the Outcome, and Create Belonging. Every 6sensor plays a part in defining the future of our industry-leading technology. 6sense is a place where difference-makers roll up their sleeves, take risks, act with integrity, and measure success by the value we create for our customers. We want 6sense to be the best chapter of your career. Senior Data Scientist, Forward Deployed Forward Deployed Engineering · Predictive track · Senior TEAM Forward Deployed Engineering (FDE), Predictive track, in Data Science & AI Engineering REPORTS TO Director of Data Science LEVEL Senior (5+ years) — the team's lead modeler LOCATION United States THE ROLE IN ONE PARAGRAPH We're hiring a Senior Data Scientist to be the team's lead modeler. You'll work hands-on with our strategic customers' data to tailor, retrain and validate the models that tell their sales and marketing teams which accounts and buyers to focus on. You'll usually drive two or three customer engagements at once, each on real production data, and present the results in terms of business value. The best of your work becomes a standard capability for every 6sense customer. What You'll Do Build for the customer Get to know each customer's go-to-market and data, from CRM and marketing automation to firmographic and intent signals. Retrain and tune account and contact models: ideal customer profile and industry definitions, keyword strategy, how CRM activity feeds the models, feature selection, and the balance of first- and third-party signals. Prove impact with backtests against the live model and, where possible, the customer's own scoring, using opportunity conversion by score bucket. Calibrate score thresholds and scaling so customers get a steady, explainable flow of qualified accounts. Build customer-ready deliverables: review decks, explainability views, score files and documentation customers can verify. Partner with the team's MLOps engineer to bring solutions into production so they keep delivering after handoff. Productize for everyone Capture what you learn as reusable patterns, and share recurring needs with Product, backed by field evidence. What You'll Bring Required: 6-8 years building and deploying machine learning models in production, ideally ranking, propensity, classification or scoring models on tabular or behavioral data. Strong foundation in machine learning and applied statistics, including tree-based models such as XGBoost and Random Forest, model evaluation and calibration. Fluent in Python and SQL, with experience on large, messy, real-world datasets, including matching customer CRM records to account data. Experience working with or for customers on their data: consulting, client-site work, or a forward-deployed, solutions or customer-facing data role. Comfortable switching between several ad hoc projects in a week rather than one long roadmap item. Able to explain technical work clearly to non-technical audiences. A product mindset: you look for the solution that works for the next ten customers, not just this one. Nice to have: B2B go-to-market data (CRM, marketing automation, intent, ABM or predictive scoring); lead or contact scoring models; model explainability methods such as feature importance or SHAP; Spark, SingleStore or Hive on AWS; LLM-based tools, agents or evaluation methods. What Success Looks Like In 3 months: you've delivered tailored models for at least two customer engagements, each with before-and-after results. In 6 months: at least one of your solutions is reusable across customers and on its way into the product. In 12 months: you're the team's go-to expert on model tuning and validation,