Specialty Capital is a data-driven alternative finance company delivering fast, flexible capital to small and mid-sized businesses. We operate at the intersection of underwriting, risk, and technology--using data and machine learning to make smarter, faster funding decisions while responsibly managing credit and fraud risk.
As the company scales, we are investing heavily in building a best-in-class internal data science and AI capability to power our next phase of growth.
The Mission
Build Specialty Capital's internal machine learning and risk modeling capability from the ground up. This role owns the design, development, and productionization of ML/AI models that directly influence underwriting decisions, fraud detection, default risk, and lead conversion.
You will act as the technical authority for applied ML across credit risk and fraud, partnering closely with leadership to translate business risk into scalable, production- grade systems with measurable financial impact.
What You'll Do:
Modeling & Analytics (Credit + Fraud)
Design, develop, and validate predictive models across:
o Credit risk and default probability
o Fraud detection and early-warning signals (e.g., synthetic identities, misrepresentation, repeat offenders, anomalous behavior)
o Funding capacity and deal sizing
o Lead scoring and submission-to-funding optimization
Apply statistical, machine learning, and ensemble techniques (e.g., logistic regression, gradient boosting, tree-based models) with a strong focus on precision/recall tradeoffs, interpretability, and real-world cost of errors.
Develop approaches that balance fraud prevention, approval rates, and customer experience.
End-to-End Model Ownership
Own the full ML lifecycle, including:
o Data exploration, profiling, and quality assessment
o Feature engineering across behavioral, transactional, temporal, and alternative data
o Model training, validation, stress testing, and bias analysis
o Deployment, monitoring, and ongoing recalibration
Define and track KPIs across risk domains (e.g., default rate, fraud loss rate, false positives, approval rate).
Operate with high autonomy, owning outcomes rather than executing predefined
tasks.
Risk & Data Innovation
Partner directly with the CDAO to:
o Integrate alternative, behavioral, and third-party data sources for both credit and fraud use cases
o Experiment with novel algorithms, hybrid rules + ML approaches, and real- time scoring frameworks
Continuously adapt models to emerging fraud patterns while maintaining stable portfolio performance.
Infrastructure & MLOps
Help design and implement the ML production stack, including:
o Cloud-based deployment (AWS or Azure)
o Real-time and batch scoring pipelines
o Model versioning, monitoring, drift detection, and retraining
o Containerization and API-based model serving (Docker, REST)
Establish best practices for model governance, reproducibility, and risk controls.
Leadership & Influence
Serve as the technical lead for applied ML across credit and fraud risk.
Partner closely with underwriting, operations, and leadership teams to operationalize model outputs.
Mentor junior data scientists and analysts as the team grows.
Shape the company's long-term ML and risk roadmap.
Who You Are - Must Haves
Experienced Risk Modeler: 3+ years in data science with hands-on experience in fintech, lending, credit risk, fraud, or MCA environments. You understand default rates, fraud loss, false positives, and submission-to-funding funnels.
Builder Mentality: Comfortable acting as the primary architect and implementer in a greenfield or lightly structured environment.
Strong Technical Foundation:
o Advanced proficiency in Python (pandas, NumPy, scikit-learn, XGBoost/LightGBM)
o Strong SQL for analytical and production workflows
o Experience with Git and collaborative development practices
Business-Aware: You design models with a clear understanding of underwriting economics, fraud tradeoffs, operational constraints, and downstream financial impact.
Nice to Have - AI, Fraud & Advanced Tooling
Fraud-Specific Experience:
o Exposure to fraud typologies, anomaly detection, network/graph-based features, or velocity rules
o Experience combining rules-based systems with ML models
Generative AI & LLMs:
o Building internal AI tools using APIs (OpenAI, Anthropic) or frameworks like LangChain
o Use cases such as merchant risk summaries, fraud review support, or underwriting policy interpretation
Explainability & Governance:
o Experience with SHAP or similar explainability techniques
o Familiarity with audit-ready or compliance-aware modeling in financial services
Why This Role Matters
Direct ownership of models that control credit risk, fraud losses, and revenue growth
High visibility and close partnership with executive leadership
Opportunity to define Specialty Capital's long-term ML and risk foundation
Job Type: Full-time
Benefits:
Paid time off
* Work from home
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