Lead Data Scientist - Growth & Marketing Models

Lead Data Scientist - Growth & Marketing Models AI-first targeting and decision models that move real money | Lean, AI-leveraged team | Senior/Lead level

• Office Locations:
  San Diego, CA (La Jolla/UTC) or Atlanta, GA (Cumberland/Galleria) or New York, NY (near Grand Central)
• Hybrid 2 days per week onsite in the office (Mondays and Thursdays), Full time M-F
• Exempt/Salary: $,000. We are open to discussing total compensation for candidates who clearly exceed the bar. Position eligible for additional incentives including bonus, 401(k) match, health and welfare benefits, amazing culture, growth opportunity and more!!

You will build the predictive models and analytics that determine whom we target, which prospects receive an offer, who we approve, and where the next dollar of marketing spend goes. Your work will ship into production and be measured against conversion, credit performance, customer economics, and profitable growth.

We are a lean data science team inside a fast-moving Fin Tech lender. We use AI as a real force multiplier: tools such as Claude, Claude Code, and ChatGPT are part of the daily workflow for analysis, coding, and drafting. Every important number and model output is verified against source data before it drives a decision. Verification-first, AI-leveraged. Our core work is customer acquisition modeling for small-business lending - direct mail and digital targeting, prescreen campaigns, and funnel economics from response through funding.

This is a high-ownership, hands-on role. Reporting and visualization support the work, but the center of gravity is production modeling, experimentation, and decisioning. You will lead projects from the business question through deployment, monitor real-world results, and mentor other data scientists.

• Targeting, response, propensity, and conversion models for direct mail, digital acquisition, and other growth channels.
• Customer segmentation, lookalike, lead-scoring, recommendation, and personalization models that improve who we contact and what we offer.
• Campaign, offer, channel, and budget optimization informed by customer lifetime value, acquisition cost, expected credit performance, and unit economics.
• Experimentation and incrementality measurement, including A/B testing, causal inference, and uplift modeling where appropriate.
• Production monitoring for model performance, drift, calibration, data quality, and retraining.

• Partner with leaders across marketing, credit risk, sales, product, and engineering to translate commercial problems into well-posed analytical questions and measurable success criteria.
• Own projects end to end: data discovery, preprocessing, feature engineering, model development, validation, deployment, monitoring, and iteration.
• Work with structured and unstructured data from disparate sources; reconcile conflicting numbers, surface data gaps, and drive issues to resolution with data owners.
• Build and evaluate supervised and unsupervised machine learning models using sound statistical methods, appropriate benchmarks, and transparent assumptions.
• Design experiments that distinguish correlation from causation and translate model lift into financial and customer outcomes.
• Collaborate with engineering and analytics partners to move models into reliable production workflows, then investigate performance changes and recalibrate, retrain, or replace models when needed.
• Communicate recommendations, tradeoffs, uncertainty, limitations, and expected business impact clearly to technical and non-technical decision-makers.
• Use AI tools to accelerate analysis, coding, documentation, and communication - while independently verifying logic, calculations, and source data before anything ships.
• Mentor other data scientists, raise modeling and coding standards, and contribute to the evolution of the analytics platform and team practices.

• Your models change targeting, offer, approval, or marketing-allocation decisions and produce measurable improvements in profitable growth.
• Models are deployed, monitored, and improved in production - not left as prototypes or slide-deck recommendations.
• Busi…