Top Data Science Jobs in India

Lead development of econometric, time series, Bayesian, and stochastic models for forecasting, pricing, and customer analytics. Translate business problems into analytical frameworks, work with large-scale data (Databricks), collaborate with stakeholders, and mentor junior team members to ensure robust, actionable statistical solutions.

Perform marketing analytics for P&C insurance campaigns: translate business questions into analytical solutions, program end-to-end in Python/SAS, run EDA, build and QC statistical and machine learning models, and generate campaign performance reports to inform decision strategy.

Perform marketing analytics for P&C insurance campaigns: convert business problems into analytical solutions, program in Python/SAS, run EDA, build and validate ML models, generate campaign and project performance reports, and QC results to support decision-making.

Validate and govern Finance & Treasury models (CCAR PPNR, URR, macroeconomic models and Non-Model Tools) through methodology review, independent testing, benchmarking, sensitivity analysis, documentation assessment, remediation tracking, and reporting. Partner with stakeholders for regulatory exams, audits, and continuous improvement of validation methodologies and governance.

Support and execute AI-driven personalization strategies across digital channels. Analyze customer behavior, prepare data, build queries and basic models, contribute to experimentation and model evaluation, and help embed personalization features with product and engineering teams to improve engagement and conversion.

Lead and manage a data science team to design, build, and deploy ML, NLP, and GenAI solutions that drive sales growth. Develop scalable data pipelines, forecasting and incentive models, GenAI use cases, KPIs, and executive-facing analytics while collaborating across Sales, Marketing, Finance, and Technology.

Develop, test, and maintain ML and Generative AI solutions for prospect targeting and acquisition. Build predictive models, RAG/LLM workflows, embeddings/semantic search, and data products. Perform feature engineering, experimentation, model evaluation, deployment support, monitoring, and ensure Responsible AI and model governance while communicating results to stakeholders.

Analyze large datasets, build and evaluate machine learning models, prepare and transform data, and create Power BI dashboards. Apply statistical methods and A/B testing, use cloud platforms (AWS, Snowflake), leverage Generative AI/LLMs, ensure data governance and responsible AI, and present insights while collaborating in an Agile team.

Lead analytics for promotions and incentives strategy, building scalable frameworks to measure promo ROI, automate performance tracking, and apply advanced analytics/ML for segmentation, targeting, and impact measurement. Analyze seller behavior and inventory patterns, partner with cross-functional teams, and translate insights into actionable business strategies to optimize pricing and promotions at scale.

Lead and mentor a team of 8-10 data scientists to define strategic roadmaps, translate business problems into data-driven solutions, and oversee end-to-end ML lifecycle (prototyping, deployment, monitoring). Drive solutions across logistics, personalization, growth, and forecasting, partner with product and engineering, hire talent, and communicate impact to senior leadership.

The job outlines the need for a Data Science Manager at Accenture, focusing on delivering innovative solutions leveraging data and AI resources.

The Principal Engineer, Data Science role involves leading data science projects and providing strategic insights using extensive IT experience.

The Senior Staff Engineer, Data Science role involves guiding team direction, solving complex challenges for clients, and leveraging technical expertise to innovate solutions.

Lead end-to-end AI/ML architecture and delivery: data ingestion, feature engineering, model development, deployment, monitoring, and MLOps. Guide technology selection, ensure scalability, security, and responsible AI practices. Mentor teams, collaborate with product and domain stakeholders, and drive continuous improvement and GenAI adoption.

Lead end-to-end design, development, deployment, and monitoring of scalable AI/ML solutions. Set architecture direction, integrate GenAI tools, enforce MLOps, mentor teams, and ensure data governance and responsible AI practices.

The role involves analyzing data to drive strategic decisions and improve employee benefits services. Requires strong analytical skills and experience in data science.

Lead a team of data scientists to leverage analytics and machine learning in the life sciences sector, optimizing outcomes through data-driven insights.

Lead insights and analysis for assigned clients using data science methods. Manage multiple analytics engagements and the delivery team, prepare and integrate datasets, communicate findings and recommendations, stay current on industry trends, and set team development and training objectives.

Lead AI and data innovation by developing ML models, optimizing data systems, and mentoring junior team members in a collaborative team environment.

Perform data analysis, preprocessing, and modeling; collaborate with the data science team to generate reports and present findings; support project monitoring and resolve data challenges under senior guidance.

Design, develop, deploy, monitor, and support a cloud-native data mart and analytics platform. Implement CI/CD, proactive monitoring, automation, and job orchestration. Troubleshoot production issues, optimize infrastructure sizing, integrate with external systems, and mentor junior engineers while participating in production support rotation.

Lead data engineering and data science initiatives: design and implement scalable ETL pipelines, extract and process data (including PDFs/APIs), use AI/ML and LLMs to enhance processing, build cloud-based data architectures, produce dashboards and automations, ensure data quality and security, mentor engineers, and partner with stakeholders to drive business value.

Lead post-deployment operations and optimization of Agentic AI, LLM integrations, and Python-based analytical pipelines. Supervise a data science operations team, ensure secure high-availability production systems, manage integrations with enterprise systems (SAP, Salesforce, Postgres), and drive continuous improvement, monitoring, and adoption of analytics solutions.

Lead advanced marketing analytics and measurement initiatives, build attribution, MMM and predictive models using structured and unstructured data, manage vendor relationships, present insights to senior stakeholders, and drive media test-and-learn and measurement roadmap execution across acquisition, engagement, and retention.

Lead design, development, and deployment of enterprise AI/ML solutions including AI agents, LLM integrations, MLOps/CI-CD, AWS-based data pipelines, production model serving and APIs, and integrations with enterprise systems. Provide technical leadership and mentor engineering teams.