Top Data Analyst Jobs in Mumbai

Supports global network management operations by overseeing third-party due diligence, onboarding and offboarding, reference data, documentation, screening, governance reporting, and audit inquiries. The role coordinates with internal teams and external parties, investigates exceptions, maintains control evidence, prepares communications and briefing materials, supports technology changes and testing, and recommends process improvements that increase accuracy, efficiency, and risk control effectiveness.

Maintains client reference data across multiple platforms, handling onboarding, record updates, workflow coordination, exception investigation, query resolution, reporting, and control adherence. Communicates with internal stakeholders, documents work, escalates issues, supports team training, and identifies process improvements. The role requires reference-data experience, strong analytical and interpersonal skills, graduate-level education, and proficiency with Microsoft Office tools. Preferred capabilities include payment processing, SWIFT, automation tools, SQL, Python, and AI-enabled process improvement.

As a Data Analyst, you'll analyze complex data sets using statistical techniques, collaborate with SMEs, and prepare reports for stakeholders.

Supports internal audits through data analytics, automation, continuous monitoring, and AI/ML model testing. Develops SQL and Python-based scripts, dashboards, exception reports, and automated testing routines; analyzes large datasets, validates findings, assesses controls and model performance, and communicates recommendations to audit and business stakeholders. The role also documents testing procedures, contributes to audit standards, resolves transactional issues, and partners with audit teams on complex engagements.

Develops dynamic client-facing reports and visualizations for investment services clients using IRD, MCD, and BI platforms. Analyzes large datasets, supports data modeling and data mart design, applies SQL for validation and optimization, and collaborates with data and product teams to ensure data integrity. Serves as an IRD subject matter expert while supporting reporting issues and complex global requirements across custody, digital, and fund services.

Analyzes trade, shipment, ERP, logistics, and landed-cost data to support global trade compliance. Builds dashboards, scorecards, recurring reports, and digital tools using SQL, Power BI, Tableau, Python, and related technologies. Supports data quality, origin and free trade agreement analyses, duty optimization, trade-flow monitoring, and risk identification. Collaborates with Trade, Supply Chain, Procurement, Finance, IT, and business teams to deliver reliable, audit-ready insights and scalable compliance solutions.

Analyze operational data and processes, identify bottlenecks, design scalable solutions, automate workflows and reporting with Python and SQL, build dashboards and monitoring, perform root-cause analysis, document logic and collaborate with product, engineering, and ops teams to improve automation and data quality.

Supports clinical trial data management through data review, cleaning, query resolution, vendor reconciliation, CRF freeze and database lock activities. Performs UAT for listings, screens, and edit checks; maintains eTMF documentation in Veeva Vault; reviews external and laboratory data; and collaborates with internal and external stakeholders while following GCP, regulatory, and clinical data management standards.

Maintains sales pipeline data quality across CRM systems by auditing, cleaning, and correcting records; resolving duplicates and stale opportunities; enforcing data standards; preparing hygiene reports, dashboards, and exception lists; supporting forecasting and pipeline reviews; documenting procedures; assisting CRM users; and identifying process improvements and automation opportunities.

Validate and transform policyholder data for actuarial modelling and assumption setting. Produce specifications, controls, documentation, and analyses; review inputs/outputs; implement remediations; support stakeholders and deliver high-quality, auditable outputs under senior guidance.

Lead a proprietary data analysis team supporting researchers, traders, and engineers. Coordinate priorities, define projects, ensure data quality, coach analysts, and personally solve complex data problems. Manage collection, tagging, cleaning, documentation, and validation of alternative, fundamental, market, and reference data. Partner with engineers to automate workflows while maintaining human review of AI-driven pipelines. Communicate findings and project status to technical and non-technical stakeholders.

Analyze courier supply and dispatch operations across onboarding, retention, availability, earnings, assignment, acceptance, and delivery success. Build reliable metrics, dashboards, and self-service reporting; identify root causes of performance changes; evaluate experiments, incentives, and product initiatives; validate data quality; and translate findings into recommendations for product, operations, and leadership.

Designs and maintains ETL pipelines integrating banking-system data into analytics platforms; builds data models for reporting, analytics, and machine learning. Collaborates with analysts, data scientists, and stakeholders to define requirements and delivers insights across risk, credit, and liabilities. Ensures data quality, security, governance, and compliance while monitoring and troubleshooting workflows and infrastructure performance.

Develop MSME commercial credit-risk models, scorecards, portfolio analytics, and predictive features using bureau data, Python, and SQL. Build reproducible datasets, validate model performance and stability, conduct portfolio diagnostics, and create client proofs of concept. Support pre-sales consulting, analytical solution design, stakeholder presentations, model governance, documentation, and productization of reusable analytics.

Develop data-driven risk and analytics products using consumer bureau and financial-services data. Responsibilities include data preparation, exploratory analysis, feature engineering, statistical and machine-learning model development, validation, documentation, reusable coding, UAT support, and productisation. The role partners with Product and Technology teams to enhance analytics capabilities, assess performance, maintain governance standards, and communicate analytical findings to technical, business, and client stakeholders.

Analyze pricing, market, customer, and performance data to identify revenue opportunities and optimize pricing strategies. Build automated dashboards and analytical tools using large datasets across Databricks, BigQuery, and Looker. Apply regression, elasticity modeling, forecasting, machine learning, and scenario analysis to generate actionable pricing recommendations. Improve workflows, ensure data quality, train stakeholders, and influence commercial decisions across regions.

The Data Analyst will collect, clean, manage, and analyze large datasets for social impact consulting and monitoring and evaluation projects. Responsibilities include statistical analysis using Excel, SPSS, or R; dashboard development; KPI tracking; reporting; data quality assurance; process audits; and delivering actionable insights. The role collaborates with project managers, M&E specialists, and cross-functional teams while supporting accurate, timely analysis and communicating findings to nontechnical stakeholders.

Maintain and execute approved SAP customer master data requests, including creation, changes, extensions, blocking, activation, and mass maintenance. Process workflows using SAP MDM or MDG, perform mass loads with SAP tools, and support data operations across business functions. The role requires strong SAP master data knowledge, prioritization, communication, and collaboration skills, with exposure to ServiceNow, Winshuttle Evolve, SQL, data management, integration, and analytics.

Analyze marketing data to measure campaign performance, ROI, attribution, customer behavior, and funnel conversion. Build Tableau dashboards and reports, define KPIs and measurement frameworks, evaluate experiments, and provide recommendations for campaign strategy and budget allocation. Translate complex findings into executive-ready presentations and collaborate with global marketing stakeholders across digital, social, email, and event channels.

Owns data stewardship for agentic AI systems, including data quality rules, lineage, observability, catalogs, semantic layers, access controls, compliance, audit trails, and data management plans. Partners with AI engineers, data scientists, legal, compliance, and business stakeholders to ensure trustworthy, compliant, and usable structured and unstructured data. Supports root-cause analysis, governance automation, and monitoring of data health and AI-related data practices.

Leads advanced analytics, KPI governance, process mapping, and digital transformation across global procurement, supply chain, fulfillment, logistics, manufacturing, and commercial operations. Develops dashboards, predictive analyses, SAP-based reporting, and actionable insights to improve inventory, fulfillment, supplier performance, forecasting, and customer service. Partners with global stakeholders on process standardization, technology adoption, change management, and continuous improvement initiatives.

Analyze structured and unstructured data to identify trends, build dashboards and reports, define KPIs, validate data quality, and provide actionable recommendations. The role requires SQL, BI visualization, Excel, relational database experience, and collaboration with business and technical stakeholders. Preferred skills include Python, ETL and data warehousing, cloud platforms, statistical analysis, forecasting, machine learning concepts, and Agile experience.

Leads a team of scientific data analysts supporting Reaxys, Embase, and future AI-enabled life sciences platforms. Oversees search and API quality, data discoverability, testing, feature releases, quality standards, and customer-focused insights. Uses scientific expertise, Python and Bash scripting, databases, cloud platforms, and AI-enabled testing workflows to improve data integrity and platform performance. Coordinates stakeholders, documents work, presents findings, and drives cross-functional projects.

Leads a team of scientific data analysts supporting Reaxys, Embase, and future AI-enabled life sciences platforms. Oversees search and API quality evaluation, data discoverability, testing, feature releases, customer-focused insights, and quality standards. Uses scientific expertise, data analysis, scripting, databases, cloud platforms, and AI-enabled testing workflows to improve platform reliability and relevance. Coordinates stakeholders, advances projects, documents work, and presents recommendations for product and data improvements.

Manage model onboarding for Citi’s RUBY forecasting platform supporting interest rate risk, CCAR, and financial forecasting. Responsibilities include gathering requirements, developing technical solutions, estimating costs, planning projects, managing risks and resources, prioritizing enhancements, executing forecast processes, and partnering with Finance, Risk, Operations, Technology, and senior stakeholders. The role requires knowledge of data modeling, interest rate risk, financial statements, forecasting, large datasets, and programming or modeling tools.