Senior Data Scientist, Forward-Deployed

6sense

. Serve as the team's lead modeler for Forward Deployed Engineering, Predictive track .

Posted 11/2/2026full-timeRemote • California • United StatesSenior💰 $168,350 - $230,913 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building and deploying machine learning models, particularly in predictive analytics and model evaluation. Proficient in Python and SQL, with a strong focus on delivering reusable solutions and effectively communicating technical concepts to diverse audiences.

Highest-signal resume keywords

Machine Learning Model DevelopmentPython ProgrammingSQL ProficiencyModel Evaluation and CalibrationCustomer Engagement and Consulting

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningApplied StatisticsRanking ModelsPropensity ModelsClassification ModelsScoring ModelsTree-Based ModelsXGBoostRandom ForestFeature Selection

Soft Skills

Clear CommunicationProduct MindsetAdaptability

Tools & Technologies

CRM SystemsMarketing AutomationSparkSingleStoreHive on AWSLLM-Based Tools

Industry Keywords

B2B Go-To-Market DataCustomer Data MatchingModel ExplainabilityAd Hoc Project ManagementForward-Deployed Solutions

Tech Stack

Tools & technologies

AWSPythonSparkSQL

About the role

Key responsibilities & impact

Serve as the team's lead modeler for Forward Deployed Engineering, Predictive track
Work hands-on with strategic customers' data to tailor, retrain, and validate models
Drive two or three customer engagements simultaneously using production data
Learn each customer's go-to-market and data, including CRM, marketing automation, firmographic, 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 first- and third-party signal balance
Prove impact with backtests against live models and, where possible, customers' own scoring
Use opportunity conversion by score bucket to measure results
Calibrate score thresholds and scaling to provide a steady, explainable flow of qualified accounts
Build customer-ready review decks, explainability views, score files, and verifiable documentation
Partner with the MLOps engineer to bring solutions into production after handoff
Capture learnings as reusable patterns and share recurring needs with Product using field evidence
Deliver tailored models for customer engagements, create reusable solutions, and develop playbooks that accelerate future engagements

Requirements

What you’ll need

6–8 years building and deploying machine learning models in production
Experience with ranking, propensity, classification, or scoring models on tabular or behavioral data
Strong foundation in machine learning and applied statistics
Knowledge of tree-based models such as XGBoost and Random Forest
Experience with model evaluation and calibration
Fluency in Python and SQL
Experience with large, messy, real-world datasets
Experience matching customer CRM records to account data
Experience working with or for customers on their data, including consulting, client-site work, or forward-deployed, solutions, or customer-facing data roles
Ability to switch between several ad hoc projects in a week
Ability to explain technical work clearly to non-technical audiences
Product mindset focused on reusable solutions
Nice to have: B2B go-to-market data, lead or contact scoring models, model explainability methods such as feature importance or SHAP, Spark, SingleStore or Hive on AWS, and LLM-based tools, agents, or evaluation methods

Benefits

Comp & perks

Generous health insurance coverage
Life insurance
Disability insurance
401(k) employer matching program
Paid holidays
Self-care days
Paid time off (PTO)
Paid parental leave
Stock options
Equipment and support for working at home or in an office
Learning and development initiatives
Access to LinkedIn Learning
Quarterly wellness education sessions
Wellness days
ERG-hosted events