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About This Role
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: $150,000\-170,000\. 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.
Salary Context
This $150K-$170K range is above the median for Data Scientist roles in our dataset (median: $160K across 258 roles with salary data).
View full Data Scientist salary data →Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 4,317 AI roles we're tracking, Data Scientist positions make up 8% of the market. At FairSquare, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $192,890 based on 789 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($160K) sits 17% below the category median. Disclosed range: $150K to $170K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
FairSquare AI Hiring
FairSquare has 1 open AI role right now. They're hiring across Data Scientist. Based in New York, NY, US. Compensation range: $170K - $170K.
Location Context
AI roles in New York pay a median of $220,000 across 1,650 tracked positions.
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
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