Top Data Analyst Jobs in Mumbai

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

Perform business process mapping and requirements elicitation (BPMN 2.0), design 'To be' solutions, produce BRDs, use cases, user journeys and acceptance criteria, support TOM and foundation data initiatives, drive process improvements using Lean/Six Sigma, document processes, conduct training, and collaborate with stakeholders to translate business needs into application and operational requirements.

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.

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 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.

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.

Design, clean, analyze, and visualize data using SQL and Python; build Tableau dashboards; apply statistics and basic ML/DS concepts; follow software/data-engineering practices (version control, unit testing, high-performance code); work with structured and unstructured data and cloud environments.

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.

Analyze and transform structured and unstructured data, identify trends, create reports and dashboards, and apply statistical and machine learning techniques. Support AI initiatives, model development, data quality validation, and deployment of analytical solutions. Collaborate with business and technical stakeholders to translate requirements into data-driven applications using .NET technologies, while documenting processes and maintaining analytical outputs.

Build and maintain Power BI dashboards, reports, data models, and DAX measures for trading and business insights. Extract, transform, validate, cleanse, and analyze data using SQL while supporting the organization’s data platform. Collaborate with stakeholders to translate business requirements into reporting solutions and progressively contribute to AWS-based data workflows, cloud storage, and data pipelines.

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.

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.

Analyze CPG/FMCG data to support pricing, distribution, sales, revenue, and commercial decision-making. Develop and optimize Power BI dashboards, reporting solutions, and analytical models; identify trends, performance gaps, pricing and distribution opportunities; track KPIs; improve data quality; automate reporting; and present actionable recommendations to cross-functional stakeholders.

Own analytics and measurement for HighLevel’s AI product portfolio. Define instrumentation standards, feature-level KPIs, adoption and quality metrics, and rigorous approaches for evaluating nondeterministic AI features. Partner with experimentation leaders, PMs, engineering, and Analytics Engineering to close data gaps, influence product roadmaps, establish canonical metrics, and mentor future analysts.

Analyze hiring and candidate data to improve decision-making, shortlisting, scoring systems, and operational efficiency. Build dashboards and reports, track hiring funnel metrics, collaborate with operations and product teams, and ensure data accuracy.

Analyze business, production, workforce, financial, and operational data to support strategic decision-making. Responsibilities include data cleaning, dashboard and KPI development, project performance analysis, forecasting, workforce planning, profitability analysis, reporting automation, and presenting recommendations to senior leadership. The role partners with Production, Talent Acquisition, Finance, HR, and Studio Operations teams.

Lead HR and people analytics by developing workforce dashboards, reports, and insights covering headcount, attrition, hiring, diversity, engagement, mobility, and costs. Support workforce planning and budgeting, ensure HR data quality and governance, automate reporting, and partner with HR, Talent Acquisition, Finance, and business leaders to provide actionable recommendations and influence workforce decisions.

Supports liquidity regulatory reporting controls by documenting data flows, business data elements, lineage, transformations, and controls from booking systems to reporting platforms. Performs SQL-based data analysis, test-case development, functional and business testing, quality remediation, root-cause analysis, requirements management, stakeholder coordination, and management reporting. Contributes to implementation activities, change control, documentation standards, and training junior team members.

Lead end-to-end MSME credit-risk analytics, including scorecard and predictive-model development, bureau feature engineering, portfolio monitoring, early-warning analysis, validation, and proofs of concept. Translate analytical findings into client-ready lending recommendations, support pre-sales engagements, review code and models, mentor analysts, and collaborate on product implementation. Ensure solutions are interpretable, stable, well-documented, compliant, and suitable for production monitoring.

Analyze MSME commercial bureau and lender portfolio data to develop credit-risk scorecards, predictive models, segmentation, early-warning analytics, and portfolio diagnostics. Engineer bureau features using Python and SQL, validate model performance and stability, build client proofs of concept, support pre-sales solution design, and contribute to model governance, documentation, UAT, and analytics productization.

Analyze large bureau and client datasets to identify trends, risks, segments, and growth opportunities. Build statistical models, benchmarks, analytical datasets, and automated workflows using Python and SQL. Translate client questions into analytical approaches, validate findings, and communicate insights through charts, reports, and presentations. Maintain documentation, reproducibility, data quality, governance, and peer-review standards while supporting market intelligence and client-facing deliverables.

Supports hosted e-discovery platforms by loading and transforming data, creating databases and user accounts, configuring permissions, investigating client technical inquiries, and assisting customers through phone, email, and screen sharing. The role also manages internal service-level tasks, collaborates with Engineering, IT, Application, and Project Management teams, and provides platform walkthroughs when needed.

Research and validate global dividend and index data using multiple sources, investigate discrepancies with data providers, perform quality audits, maintain process and compliance documentation, and collaborate with global stakeholders. The role also supports automation, tool enhancements, data quality initiatives, and index operations while working rotational shifts in Mumbai.