Sr. Data Scientist, Forward-Deployed

6sense•San Francisco, CA

8d•$168,350 - $230,913•Remote

About The Position

6sense is hiring a Senior Data Scientist for its Forward Deployed Engineering team, Predictive track, in Data Science & AI Engineering. The role is the team's lead modeler and focuses on tailoring, retraining, and validating predictive models using strategic customers’ production data to help sales and marketing teams prioritize accounts and buyers. The scientist typically drives two or three customer engagements at a time and presents results in terms of business value. Successful work may be developed into standard capabilities for all 6sense customers. The position combines customer-specific modeling with reusable product development. It involves understanding customers’ go-to-market operations and data, improving account and contact models, validating impact against live or customer models, calibrating scores, preparing customer-ready materials, and partnering with MLOps to put solutions into production. The role also captures reusable patterns and shares recurring customer needs with Product, supported by field evidence. The position is based in the United States and is identified as remote in the posting. It reports to the Director of Data Science. The stated base salary range is $168,349.50–$230,912.60; actual salary may vary based on factors including work location and experience. Other compensation may include a bonus program or commission plan and stock options if approved. 6sense describes its mission as multiplying growth, retention, and efficiency, and emphasizes teamwork, curiosity, integrity, ownership, and belonging.

Requirements

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.
Fluency in Python and SQL.
Experience working with large, messy, real-world datasets, including matching customer CRM records to account data.
Experience working with or for customers on their data, such as consulting, client-site work, or a forward-deployed, solutions, or customer-facing data role.
Comfort switching between several ad hoc projects in a week rather than focusing on one long roadmap item.
Ability to explain technical work clearly to non-technical audiences.
A product mindset focused on solutions that can work for future customers, not only the current customer.

Nice To Haves

Experience with B2B go-to-market data, including CRM, marketing automation, intent, ABM, or predictive scoring.
Experience with lead or contact scoring models.
Experience with model explainability methods such as feature importance or SHAP.
Experience with Spark, SingleStore, or Hive on AWS.
Experience with LLM-based tools, agents, or evaluation methods.

Responsibilities

Learn each customer’s go-to-market operations and data, including CRM, marketing automation, firmographic data, and intent signals.
Retrain and tune account and contact models, including ideal customer profile and industry definitions, keyword strategy, CRM activity inputs, feature selection, and the balance of first- and third-party signals.
Demonstrate model impact through backtests against the live model and, where possible, customer scoring, using opportunity conversion by score bucket.
Calibrate score thresholds and scaling to provide customers with a steady, explainable flow of qualified accounts.
Create customer-ready deliverables, including review decks, explainability views, score files, and documentation customers can verify.
Partner with the team’s MLOps engineer to put solutions into production and support their continued delivery after handoff.
Capture learnings as reusable patterns and share recurring customer needs with Product, supported by field evidence.
Drive multiple customer engagements and communicate results in terms of business value.

Benefits

Health coverage and generous health insurance coverage.
Life insurance.
Disability insurance.
401(k) employer matching program.
Paid holidays.
Paid parental leave.
Generous paid time off (PTO).
Quarterly self-care days off and wellness days.
Stock options, subject to applicable approval.
Equipment and support for working and connecting with teams at home or in an office.
Learning and development initiatives, including access to LinkedIn Learning.
Wellness education sessions and employee resource group events.