Data Scientist / ML Engineer Risk & Analytics
Data Scientist / ML Engineer Risk & Analytics CreditSea | Raipur, Chhattisgarh About the role CreditSea disburses 300cr+ every month across our NBFC partners and is scaling fast. Our Risk & Analytics team already owns underwriting models, collections scoring, and fraud detection on top of our in-house LOS/LMS we're now looking to strengthen it as we scale further. We're not looking for someone who can only build models in a notebook. We're looking for someone who can ship a model into production and understand why it's making the decisions it's making. If you can write clean code, reason about credit risk like a business owner, and don't need someone else to translate "the business problem" into "the technical problem" for you this is your role. What you'll do Join our existing Risk & Analytics team to build and ship ML models for credit underwriting, collections prioritization, and early-warning/fraud detection using bureau data, alternate data (transaction patterns, app behavior, account aggregator data), and behavioral signalsOwn models end-to-end: from feature engineering through production deployment, monitoring, and retraining not just the research phaseRaise the bar on explainability across our existing models (SHAP/LIME-level interpretability) not as an afterthought, but because credit decisions need to be defensible to a regulator, a customer, and your own risk teamWork directly with underwriting, collections, and product teams to understand what's actually breaking in the business, and turn that into model improvementsPartner with data engineering to strengthen our pipelines off our LOS/LMS, payment rails, and bureau feeds What we're looking for Strong coding fundamentals you can write production-quality Python, not just prototype in a notebookReal ML/statistics chops comfortable with GBMs, logistic regression, and knowing when not to reach for a deep modelPreferred technical skill: hands-on experience with XGBoost for building and tuning high-performance credit risk and fraud modelsGenuine curiosity about the business, not just the model. We'll ask you in the interview to explain a past project from the business's point of view, not just the architectureExperience (or strong interest) in fintech, lending, or payments is a big plus bonus if you understand NPA cycles, collections economics, or credit bureau dataIdeally time at an early-stage startup where you had to make product/business trade-offs directly, not just execute a specComfortable with ambiguity you'll be improving and extending an existing function, not just following a fixed playbook, and we want someone who'll push back on how things are done today Bonus points You've built something end-to-end yourself a side project, a small business, anything where you owned the whole loop from idea to real usersYou've worked with alternate/behavioral credit data beforeYou have opinions about model governance and explainability, and can talk about why they matter beyond "the regulator says so" What you'll get Real ownership from day one you're not the 500th data scientist at a big company, you're joining a small, high-leverage team where your work ships fastDirect access to leadership this team reports into a founder-led risk functionA chance to work on problems that actually move the business: better underwriting means better unit economics, not just a better AUC score .
