Lead Data Scientist - Growth & Marketing Models.

Lead Data Scientist - Growth & Marketing Models

• 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: $150,000-200,000. 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!!

The opportunity

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 FinTech 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.

What You'll Build

• 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.

What You'll Do

• 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.

What Success Looks Like

• 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.

• Business partners understand what the model is doing, when to trust it, and where its limitations begin; assumptions and results can be reproduced and defended.

• The team becomes faster and more rigorous because of the standards, tools, and mentoring you bring.

Who You Are

• A proactive owner of ambiguous problems. You form a view, show your assumptions, make progress without perfect information, and adjust when the evidence changes.

• Quantitatively strong and fluent in predictive models, experiments, uncertainty, and business economics.

• Verification-minded. You do not take a number - yours, a vendor's, or an AI's - at face value.

• Motivated by measurable impact and comfortable being accountable for whether a model works after launch.

• Detail-oriented without losing the commercial big picture, and able to move quickly without lowering the quality bar.

• A clear communicator who is AI-native but not AI-dependent: you use modern tools to move faster while retaining independent judgment and ownership of the output.

What You'll Need

• Master's degree or higher in statistics, mathematics, computer science, engineering, operations research, economics, or another quantitative discipline — or equivalent hands-on experience shipping production models.

• 5+ years of relevant data science or machine learning experience, or an equivalent combination of education and experience.

• Strong programming skills in Python or R, plus proficiency in SQL and relational databases.

• Demonstrated experience with supervised and unsupervised machine learning, statistical analysis, model validation, feature engineering, and experimental design.

• Experience building, deploying, monitoring, and maintaining predictive or recommendation models in a live environment.

• Strong programming practices, including version control (for example, Git), reproducible analysis, testing, and documentation.

• Experience leading end-to-end data science projects, coordinating stakeholders independently, and mentoring other data scientists.

• Strong written and verbal communication across technical and non-technical audiences.

Especially Relevant Experience

You do not need every item below. These experiences are particularly relevant to the work:

• Growth data science, marketing analytics, customer acquisition, targeting, response modeling, propensity modeling, lead scoring, segmentation, recommendation systems, or personalization.

• Direct mail, performance marketing, digital acquisition, cross-sell, retention, customer lifetime value, marketing attribution, or offer optimization.

• A/B testing, causal inference, uplift modeling, incrementality measurement, or optimization under business constraints.

• FinTech, consumer lending, credit risk, underwriting, pricing, AWS, cloud technology, or production machine learning / MLOps.

Why FairSquare?

• Positive, energetic, passionate, business casual environment with management who are committed to your success.
• We’re committed to fostering talent and providing opportunities for personal and professional growth.
• Health insurance for you and your family, matching 401K retirement plans, and education stipends.
• Numerous employee events throughout the year, including our annual traditions such as a Day at the Del Mar Racetrack, Holiday Party, Concerts & Sporting Events and more.

FairSquare serves the small business community. Since 1999, we have provided more than $3 billion in funding to over 50,000 customers to support their working capital and equipment financing needs. We are one of the country's largest private providers of small business loans, having funded more than $3 billion to help small businesses grow. Our personal approach helps strengthen small business owners and we pride ourselves on being a resource they can trust. We are believers in small business owners.

FairSquare is an Equal Opportunity Employer.