About Specialty Capital Specialty Capital is a data-driven alternative finance company delivering fast adaptable 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 reliable 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 About Specialty Capital Specialty Capital is a data-driven alternative finance company delivering fast adaptable capital to small and mid-sized businesses We operate at the intersection of un

📌 Senior Data Scientist (remote) (Karnataka)
🏢 Specialty Capital
📍 India