{"id":3565535,"url":"https://alion.io/job/paramount-consulting-senior-data-scientist-credit-risk-modelling","title":"Senior Data Scientist - Credit Risk Modelling","company":{"id":2674106,"name":"Paramount Consulting","domain":"paramountconsulting.in","url":"https://alion.io/company/paramount-consulting","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"Data Science","role_family":"Data Science","seniority":"senior","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Mumbai, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":19000,"max_usd":41000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":75},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"CatBoost","optional":false},{"name":"Git","optional":false},{"name":"LightGBM","optional":false},{"name":"LLM","optional":false},{"name":"MLFlow","optional":false},{"name":"NLP","optional":false},{"name":"Python","optional":false},{"name":"SQL","optional":false},{"name":"Time Series","optional":false},{"name":"XGBoost","optional":false},{"name":"Machine Learning","optional":true},{"name":"RAG","optional":true}],"status":"live","first_seen_at":"2026-10-31T09:26:06Z","employer_posted_date":null,"last_verified_at":"2026-10-31T09:26:06Z","board_verified":false,"closed_at":null,"days_open":5,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":5},"description":"About the role:\n\n- Own end-to-end development of credit scorecards and decision analytics across bureau, platform, behavioural and portfolio data - from population and target definition through deployment and monitoring.\n\n- Work with Credit, Risk, Underwriting and Technology to convert model outputs into grades, approval treatment, limit, pricing, tenure and reason codes. Applied AI is a selective secondary capability for document, evidence and analytical assistance - not autonomous financial decisioning.\n\nKey Responsibilities:\n\n- Define development populations, observation/performance windows and targets using portfolio maturity, vintage, roll-rate and business context; benchmark existing scores before recommending a new model or recalibration.\n\n- Clean and profile data, engineer interpretable features, prevent leakage and build explainable benchmark and challenger models across bureau, platform, repayment and other approved data.\n\n- Complete champion-challenger selection and validation using KS, Gini/AUC, calibration, stability/PSI, out-of-time and segment performance; create score scaling, grades, reasons, limitations and model documentation.\n\n- Translate selected models into policy/BRE treatment, approval/referral/rejection, limit, pricing and tenure logic; prepare deployment artefacts, golden cases, API/UAT evidence and production-monitoring requirements.\n\n- Develop EWS, collections, fraud/trust, propensity and portfolio analytics, and selectively support grounded NLP/LLM use cases such as document extraction, evidence retrieval and internal risk summaries.\n\nCore Competencies:\n\n- Strong statistical discipline combined with practical credit judgement - able to distinguish predictive lift from leakage, instability or weak business meaning.\n\n- Hands-on ownership mindset: comfortable coding, challenging data, presenting decisions and following models through production monitoring.\n\n- Clear communicator who can explain model behaviour, limitations and business impact to Credit, Underwriting, Technology, management and assurance teams.\n\n- Understanding of detailed data statistics methods like regression, time series, sampling theory, hypothesis testing etc.\n\nMust-Have Requirements:\n\n- 5 - 8 years of hands-on Data Science / statistical modelling experience, including at least 3 years in lending, credit risk, underwriting or closely related BFSI analytics; strong Python and SQL are mandatory.\n\n- Personally built at least one credit scorecard or underwriting model end-to-end, including population/target design, feature engineering, validation, calibration, deployment support and monitoring.\n\n- Strong understanding of bureau and lending data, model governance, explainability, reason codes and production implementation through policy/BRE, APIs or decision engines.\n\n- Exposure to logistic scorecards and ML methods such as XGBoost/CatBoost/LightGBM, along with cloud, MLflow, APIs, Git and MLOps practices.\n\n- B.E/B.Tech/B.Stat.\n\nGood-to-Have (Optional):\n\n- Experience in MSME, embedded finance, line-of-credit, working-capital, collections, fraud or early-warning analytics.\n\n- Practical exposure to NLP/LLM, RAG or document-intelligence use cases with grounding, evaluation, privacy controls and mandatory human review.\n\nIdeal Candidate Profile:\n\n- A credit modeller first: technically strong, commercially aware and able to translate statistics into defensible lending treatment.\n\n- Has worked with imperfect real-world data and can make transparent decisions on exclusions, missingness, stability, segments and implementation trade-offs.\n\n- Uses Applied AI selectively where it improves analysis or productivity, without over-engineering the role or weakening model and policy governance.\n\nSkills\nData Science, Analytics, Risk Analytics, Credit Risk, Python, SQL, Statistics, Predictive Modeling, Machine Learning, Risk Modeling, Data Scientist","description_format":"text","description_chars":3892,"description_truncated":false,"requirements":{"experience_years_min":5,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["RegTech, AML & Compliance","Business 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