Staff Data Scientist, Forward Deployed

6sense•San Francisco, CA

7d•Remote

About The Position

The Staff Data Scientist, Forward Deployed, leads technical work across strategic customer engagements, partnering directly with customers to tailor predictive models to their go-to-market needs and improve pipeline outcomes. The role sets technical direction for five to ten engagements at a time while remaining hands-on, and brings recurring customer needs and successful solutions back into the product. The position works with customers' marketing and revenue operations teams to understand their business, data, and goals; tailor and optimize predictive models; and guide solutions from discovery through production. It also collaborates with Professional Services and Customer Success to scope and measure engagements, presents model recommendations supported by customer-verifiable analysis, and helps customers understand model and configuration changes. The Staff Data Scientist identifies reusable patterns for the product roadmap, sets standards for discovery, validation, documentation, and handoff, and mentors data scientists. Success includes measurable predictive improvements for customers, productizing solutions, and building tooling and playbooks that help future engagements move faster.

Requirements

10 or more years building and deploying machine learning models in production, ideally supervised models on tabular or behavioral data such as propensity, scoring, or ranking models.
Experience working directly with customers on their data, such as consulting, client-site work, or a forward-deployed or solutions role.
Strong applied machine learning fundamentals, including tree-based models such as XGBoost and Random Forest, feature engineering, evaluation, calibration, threshold setting, and critical evaluation of metrics.
Experience owning production model changes end to end, including backtesting, promotion decisions, and rollback.
Fluency in Python and SQL.
Comfort working with messy, fragmented customer data rather than only clean benchmarks.
Ability to manage two or three engagements at once and switch between them without a fixed sprint plan.
Exceptional communication skills, including explaining work to both executives and engineers.
A product mindset, with an inclination to build reusable capabilities rather than repeatedly solve the same problem by hand.

Nice To Haves

Experience with B2B go-to-market data, including CRM, marketing automation, intent, ABM, or predictive scoring.
Experience explaining model output to non-technical users.
Experience leading a small team or a portfolio of client engagements.
Experience building or evaluating LLM-based tools and agents.
Experience with data platforms such as SingleStore, Hive, or Spark on AWS.

Responsibilities

Lead the technical side of strategic customer engagements from discovery through a production solution.
Partner with customers' marketing and revenue operations teams to understand their go-to-market strategy, data, and goals, and identify opportunities for predictive intelligence.
Tailor and optimize customer models, including ideal customer profile and industry definitions, keyword and intent strategy, CRM and marketing automation activity inputs, and score thresholds.
Work with Professional Services and Customer Success to scope, plan, estimate, and define success measures for engagements.
Lead customer reviews recommending next models, using backtests and conversion analysis that customers can verify in their own data.
Communicate the work and its results in language that resonates with business leaders and technical practitioners.
Guide customers through model and configuration changes and prepare their teams for what to expect.
Turn successful approaches into reusable patterns and bring recurring patterns to Product as roadmap input supported by field evidence.
Set team standards for discovery, validation, documentation, and handoff.
Mentor the team's data scientists.