{"id":3347955,"url":"https://alion.io/job/idfc-first-bank-senior-data-analyst-retail-liabilities","title":"Senior Data Analyst (Retail Liabilities)","company":{"id":720227,"name":"IDFC FIRST Bank","domain":"idfcfirst.bank.in","url":"https://alion.io/company/idfcfirst","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"Analytics","role_family":"Analytics","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":15500,"max_usd":31000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":25},"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Machine Learning","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"Spark","optional":true}],"status":"live","first_seen_at":"2026-10-27T17:35:30Z","employer_posted_date":null,"last_verified_at":"2026-10-27T17:35:30Z","board_verified":false,"closed_at":null,"days_open":2,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":2},"description":"We are looking for a Data Analyst/Senior Data Analyst with strong expertise in credit risk analytics, predictive modeling, and statistical analysis to develop and optimize risk models using large-scale financial datasets. The ideal candidate will have hands-on experience in Python, PySpark, feature engineering, credit risk scorecards, and machine learning, with the ability to translate complex data into actionable business strategies and measurable financial outcomes.\n\nResponsibilities: \nData Analytics and Engineering: Clean, process, merge, and analyze large-scale financial datasets to develop high-quality analytical solutions and credit risk models.\nFeature Engineering: Identify, create, and select meaningful variables from financial and customer data to improve predictive model performance.\nCredit Risk Modelling: Develop, validate, and optimize application and behavioral scorecards using statistical and machine learning techniques.\nModel Deployment and Monitoring: Support model deployment, track performance, conduct backtesting, and recalibrate models to maintain accuracy and reliability.\nBusiness Strategy and Insights: Translate analytical findings into actionable recommendations for credit policies, credit limit optimization, delinquency management, and portfolio profitability.\nModel Governance and Compliance: Ensure models meet internal governance standards, regulatory requirements, and expectations for transparency and traceability.\nStakeholder Management: Collaborate with business, risk, and analytics teams to communicate model performance, analytical insights, and recommendations.\n\nKey Success Metrics: \nImprove credit risk model accuracy and predictive performance.\nSupport better credit decisions and reduce portfolio default risk.\nDrive portfolio profitability through data-backed strategies.\nDeliver reliable, scalable, and well-governed analytical models.\nContinuously improve model performance through monitoring and recalibration.\n\nRequirements: \nProven experience in developing and deploying credit risk models (e. g., application scoring, behavioral scoring).\nSolid understanding of credit risk assessment techniques, including statistical, machine learning, and traditional risk modeling approaches.\nExperience in using data science and statistical software (e. g., Python, PySpark) for data analysis and model development.\nStrong communication skills, with the ability to present complex analytical results to non-technical stakeholders.\nExperience in model deployment practices.\n\nPreferred Qualifications: \nFull-time bachelor's or master's degree in engineering, computer science, statistics, mathematics, economics, finance, or related disciplines.\nRelevant professional experience in credit risk analytics, scorecard development, statistical modelling, or predictive analytics.\nExperience working with banking or financial datasets.\nStrong communication and stakeholder management skills.\nSkills\nAnalytics, Credit Risk, Data Analyst, PySpark, Python, 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