Data Analyst

Responsibilities:

• Design, develop, and maintain interactive dashboards and reports using Tableau, Power BI, or similar BI tools to support business stakeholders.
• Write complex SQL queries to extract, transform, validate, and reconcile data from relational databases and data warehouses.
• Develop Python-based automation scripts for repetitive reporting, data processing, data quality checks, and reconciliation workflows.
• Perform exploratory data analysis to identify trends, patterns, outliers, and business drivers across large BFSI datasets.
• Build and validate basic-to-intermediate data science models such as classification, regression, forecasting, segmentation, propensity scoring, and anomaly detection models.
• Translate business problems into analytical frameworks, define target variables, engineer features, evaluate model performance, and explain model outputs in business-friendly language.
• Work on use cases such as delinquency/bounce prediction, collections strategy, branch performance risk flags, portfolio yield analysis, and operational exception detection.
• Create model monitoring and performance tracking reports to measure lift, stability, drift, accuracy, recall, precision, and business impact over time.
• Collaborate with business, product, operations, risk, collections, finance, and technology teams to convert insights into actionable interventions.
• Track analytics tasks, sprints, documentation, and knowledge repositories using JIRA and Confluence.
• Present analytical findings and recommendations clearly to both technical and non-technical audiences, including senior leadership.

Requirements:

• SQL Must-Have: Advanced querying, joins, CTEs, window functions, stored procedures, data validation, and query optimisation.
• Python + Git: Must have Pandas, NumPy, automation scripting, data wrangling, reusable code, version control, and basic packaging discipline.
• Data Science / ML Must-Have / Strong.

Good-to-Have:

• Scikit-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.
• Statistics Must Have: Hypothesis testing, correlation, distributions, sampling, outlier treatment, cohort analysis, trend analysis, and variance/driver analysis.
• AWS Redshift Must-Have: Data warehousing, cluster usage awareness, query performance tuning, and working with large production datasets.
• Automation and ETL: Must-have scripted pipelines, scheduled jobs, reconciliation checks, exception reporting, and ETL automation.
• BI / Dashboarding Must Have Tableau / Power BI dashboard design, KPI tracking, drill-down views, stakeholder-ready reporting, and report publishing.
• Good-to-Have: Ability to convert models into repeatable scoring scripts/batch jobs, document assumptions, and coordinate with engineering teams for productionization.
• Documentation: Must Have Clear documentation of logic, assumptions, data definitions, model methodology, limitations, and business interpretation using Confluence or similar tools.

Non-Technical Skills:

• Problem Solving: Ability to break down complex business problems and design data-driven solutions with minimal supervision.
• Business Translation: Ability to convert model outputs into simple business actions, not just technical metrics.
• Stakeholder Management: Proven ability to communicate insights clearly to diverse audiences, including senior leadership, product teams, operations, risk, and collections.
• Ownership Mindset: Strong follow-through on data accuracy, timelines, documentation, and stakeholder closure.
• Communication: Strong written and verbal communication skills with the ability to simplify complex analysis.
• Agility: Comfortable working in fast-paced environments and managing multiple priorities simultaneously.

Preferred Background:

• 1-5 years of experience in analytics, business intelligence, or data science roles, preferably in BFSI / NBFC / lending/fintech.
• Experience working with loan, customer, collections, risk, operations, sales, finance, or portfolio datasets will be an advantage.
• Exposure to production reporting environments and large-scale data warehouses will be preferred.
• The 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.

Skills

Python, SQL