Lead Data Scientist (Marietta, GA, 30067)

CAN Capital • Marietta, GA

21h

About The Position

CAN Capital is seeking a Lead Data Scientist to design, build, and deploy proprietary credit risk models that directly drive underwriting decisions, portfolio performance, and scalable growth across our working capital lending and equipment financing businesses. This is a senior individual contributor role with the opportunity to grow into a team leadership position as our data science function scales. You will own the development of next-generation models — including Gradient Boosting (LightGBM), ensemble methods, and challenger architectures — leveraging rich inputs from credit bureau data (Experian, LexisNexis), cash flow and bank statement analysis (Ocrolus), and internal loan performance data. A significant portion of this work will involve modernizing and augmenting our existing Logistic Regression and LGBM decisioning stack, as well as building foundational models for our newly acquired equipment financing business. Beyond core credit modeling, you will play a key role in shaping our broader AI strategy — identifying, evaluating, and helping prioritize high-impact use cases across underwriting, operations, and customer lifecycle management. Success in this role is defined by your ability to build production-ready models, translate data science into tangible business outcomes, and bring intellectual leadership to an evolving analytics function.

Requirements

• 5+ years of experience in credit risk modeling; fintech, alternative lending, or small business lending strongly preferred
• Proven track record building and deploying machine learning models in production decisioning environments
• Hands-on experience with credit bureau data (Experian and/or LexisNexis), including feature construction from tradeline and bureau attributes
• Strong Python skills, including experience with ML libraries (scikit-learn, LightGBM, XGBoost) and data manipulation (pandas, numpy)
• Strong SQL skills; experience with cloud data warehouses (Amazon Redshift or similar)
• Experience building features from complex, raw data sources including cash flow, transaction, or bank statement data
• Ability to manage multiple projects simultaneously and deliver to key milestones in a fast-paced environment
• Excellent communication skills — ability to translate complex model outputs and methodology to non-technical stakeholders ​

Nice To Haves

• Experience with Ocrolus, Clarity Services, or similar alternative/cash flow data vendors
• Familiarity with equipment financing or small business lending credit dynamics
• Experience with partner/broker channel lending and the model implications of indirect origination
• Knowledge of model risk management frameworks, fair lending considerations, or regulatory model governance
• Exposure to applying AI solutions (e.g., LLMs, automation) to real-world business processes
• Prior experience in an early-stage or scaling analytics function, including building tooling and infrastructure

Responsibilities

• Credit Risk Modeling Own the development and ongoing improvement of proprietary credit risk models, including probability of default (PD), approval, and risk segmentation models, across working capital and equipment financing products
• Apply and evaluate modern ML techniques including LightGBM, other Gradient Boosting frameworks, Random Forests, and Logistic Regression; design challenger models to benchmark against production incumbents
• Build models for the equipment financing business, working pragmatically with limited historical data through techniques such as transfer learning, proxy variables, and external benchmarks
• Perform advanced feature engineering using: Credit bureau data (Experian, LexisNexis) including tradeline, derogatory, and inquiry attributes Cash flow and bank statement data (Ocrolus) for revenue and repayment capacity signals Internal loan performance data, including repeat-customer behavioral features External and alternative data sources as appropriate
• Model Deployment & Production Partner with Technology to deploy models into production decisioning environments, ensuring models are scalable, auditable, and aligned with business objectives
• Build and maintain scalable, repeatable modeling pipelines
• Design and execute model performance monitoring frameworks using metrics such as AUC, KS, Gini, and business KPIs; report on performance, drift, and stability over time
• Implement model explainability techniques (e.g., SHAP, feature importance) to support risk, compliance, and fair lending requirements
• Maintain documentation and controls in support of model risk management and regulatory auditability standards ​
• AI Strategy & Innovation Partner with business and technology leadership to identify and evaluate high-impact AI use cases across underwriting, operations, and customer lifecycle management
• Help define the data science and AI roadmap as the function grows, bringing a practical, ROI-focused perspective to prioritization
• Stay current on advances in applied ML and AI relevant to fintech lending; bring new ideas and methods to the team ​

Benefits

• Day-one health benefits
• Generous PTO and Holidays
• 401(k) with company match
• Comprehensive health and financial protection coverage for you & your family

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed
• Number of Employees: 101-250 employees