{"id":4335011,"url":"https://alion.io/job/capri-global-capital-data-analyst","title":"Data Analyst","company":{"id":996,"name":"Capri Global Capital","domain":"cgcl.co.in","url":"https://alion.io/company/capri-global-capital","size_band":"5000+","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":null,"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Anomaly 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stakeholders.\nWrite complex SQL queries to extract, transform, validate, and reconcile data from relational databases and data warehouses.\nDevelop Python-based automation scripts for repetitive reporting, data processing, data quality checks, and reconciliation workflows.\nPerform exploratory data analysis to identify trends, patterns, outliers, and business drivers across large BFSI datasets.\nBuild and validate basic-to-intermediate data science models such as classification, regression, forecasting, segmentation, propensity scoring, and anomaly detection models.\nTranslate business problems into analytical frameworks, define target variables, engineer features, evaluate model performance, and explain model outputs in business-friendly language.\nWork on use cases such as delinquency/bounce prediction, collections strategy, branch performance risk flags, portfolio yield analysis, and operational exception detection.\nCreate model monitoring and performance tracking reports to measure lift, stability, drift, accuracy, recall, precision, and business impact over time.\nCollaborate with business, product, operations, risk, collections, finance, and technology teams to convert insights into actionable interventions.\nTrack analytics tasks, sprints, documentation, and knowledge repositories using JIRA and Confluence.\nPresent analytical findings and recommendations clearly to both technical and non-technical audiences, including senior leadership.\n\nRequirements: \nSQL Must-Have: Advanced querying, joins, CTEs, window functions, stored procedures, data validation, and query optimisation.\nPython + Git: Must have Pandas, NumPy, automation scripting, data wrangling, reusable code, version control, and basic packaging discipline.\nData Science / ML Must-Have / Strong.\n\nGood-to-Have: \nScikit-learn, statsmodels, regression, classification, clustering, time-series forecasting, feature engineering, train-test validation, cross-validation, confusion matrix, ROC-AUC, precision, recall, lift, and model explainability basics.\nStatistics Must Have: Hypothesis testing, correlation, distributions, sampling, outlier treatment, cohort analysis, trend analysis, and variance/driver analysis.\nAWS Redshift Must-Have: Data warehousing, cluster usage awareness, query performance tuning, and working with large production datasets.\nAutomation and ETL: Must-have scripted pipelines, scheduled jobs, reconciliation checks, exception reporting, and ETL automation.\nBI / Dashboarding Must Have Tableau / Power BI dashboard design, KPI tracking, drill-down views, stakeholder-ready reporting, and report publishing.\nGood-to-Have: Ability to convert models into repeatable scoring scripts/batch jobs, document assumptions, and coordinate with engineering teams for productionization.\nDocumentation: Must Have Clear documentation of logic, assumptions, data definitions, model methodology, limitations, and business interpretation using Confluence or similar tools.\n\nNon-Technical Skills: \nProblem Solving: Ability to break down complex business problems and design data-driven solutions with minimal supervision.\nBusiness Translation: Ability to convert model outputs into simple business actions, not just technical metrics.\nStakeholder Management: Proven ability to communicate insights clearly to diverse audiences, including senior leadership, product teams, operations, risk, and collections.\nOwnership Mindset: Strong follow-through on data accuracy, timelines, documentation, and stakeholder closure.\nCommunication: Strong written and verbal communication skills with the ability to simplify complex analysis.\nAgility: Comfortable working in fast-paced environments and managing multiple priorities simultaneously.\n\nPreferred Background: \n1-5 years of experience in analytics, business intelligence, or data science roles, preferably in BFSI / NBFC / lending/fintech.\nExperience working with loan, customer, collections, risk, operations, sales, finance, or portfolio datasets will be an advantage.\nExposure to production reporting environments and large-scale data warehouses will be preferred.\nThe candidate should help the organisation move beyond static MIS reporting towards insight-led analytics, automated decision support, and practical data science models that improve business monitoring, prioritisation, risk identification, productivity, and leadership decision-making.\nSkills\nPython, SQL","description_format":"text","description_chars":4553,"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":["Business & SME Lending","Mortgage Lending & Brokers"],"lifecycle":[{"event":"open","at":"2026-11-15T10:35:00Z"}],"visa":[],"liveness":{"score":86,"band":"hot","label":"Hiring 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