Top Data Science Jobs in India

The role involves advanced work in AI and machine learning, focusing on data science responsibilities within KPMG, catering to various sectors.

The role involves leading the design and implementation of scalable data architecture and platforms, guiding teams in alignment with business objectives and driving data strategy and governance.

Manage a team overseeing data annotation processes and quality for machine learning systems. Collaborate with vendors, establish quality benchmarks, and improve workflows.

Lead execution and roadmap for the AI Use Case Insights product: translate user feedback into requirements, prioritize backlog, partner with engineering/design/data science, launch AI workflows, track adoption and outcomes, and shape product strategy for enterprise decision-makers.

Lead analytics projects to improve client retention across content, interactions, and events. Analyze large datasets, partner with data scientists and engineers to build scalable solutions, automate reporting, and present insights to business leaders. Own problem structuring, stakeholder engagement, and deliver cross-organizational analytics.

Lead and grow a regional Data & AI technical solutions team in India to ensure resiliency and performance of customer production workloads across APJ and EMEA. Oversee support operations, resolve complex data engineering and ML/streaming incidents, optimize performance, stabilize workloads on new features, engage with customers and product teams, and develop team technical expertise.

The Senior Product Manager will drive product strategy and development, collaborating with cross-functional teams to enhance customer experience, focusing on AI and SaaS solutions.

Work on large-scale manufacturing datasets to build and deploy ML and statistical models. Extract and clean data from varied sources, apply feature engineering, and use tools like Python/R, Spark, Hadoop, and TensorFlow. Communicate results, automate analyses, and collaborate on industrial AI solutions; experience with time-series, images, semi-supervised learning, and MES is a plus.

Lead and mentor Predictive and Causal AI teams, architect end-to-end ML/GenAI solutions (including RAG and agentic frameworks), drive MLOps and cloud deployments, prototype POCs to demonstrate ROI, consult global stakeholders, and shape center-level AI product strategy and talent programs.

Design, deploy, and scale production-grade machine learning, deep learning, and generative AI solutions on cloud platforms, primarily Azure. Lead LLM applications such as chatbots and agentic workflows, oversee the full ML lifecycle, ensure production reliability and governance, and partner with global business and technical stakeholders. Mentor junior team members, influence architecture and best practices, and drive AI initiatives from problem definition through measurable business impact.

Lead the architecture, design, and delivery of scalable Python platforms supporting data science and analytics. Responsibilities include defining architecture standards, developing with Django and Flask, designing microservices, integrating cloud technologies, supporting ML workloads, overseeing CI/CD, Docker, Kubernetes, and cloud deployments, and managing engineering teams. The role is customer-facing and requires technical leadership, stakeholder communication, and delivery ownership.

Architects, develops, and deploys advanced AI and mathematical optimization systems for travel platforms. Integrates machine learning, LLMs, generative AI, and MLOps into high-availability products supporting airline pricing, network planning, agency automation, and airport operations. The role leads research innovation, modernizes legacy workflows on GCP, establishes AI lifecycle standards, partners with product and architecture leadership, promotes agentic AI adoption, and mentors data scientists and engineers.

Lead end-to-end product lifecycle for healthcare-focused data science, cloud, and analytics products. Define strategy, prioritize backlog, translate business needs into technical requirements, collaborate with engineering and data science to design and deploy AI/ML solutions, support launches, and measure product performance.

Senior strategic leader responsible for driving software engineering excellence and accelerating AI adoption across R&D. Define and execute Software & AI roadmaps, implement DevSecOps/CI/CD and SDLC best practices, oversee end-to-end AI/ML lifecycle, build high-performing teams, and lead digital transformation, compliance, and stakeholder alignment to deliver scalable, secure, regulated MedTech software solutions.

Manage data science and machine learning initiatives using Python, advanced analytics, statistical methods, and large datasets. Develop models for pricing, forecasting, segmentation, experimentation, and business optimization. Translate complex analyses into clear recommendations, lead methodological research, and communicate effectively with stakeholders. Balance hands-on technical delivery with strategic business impact while managing ambiguous problems and stakeholder expectations.

Leads data science and machine learning initiatives involving pricing analytics, forecasting, segmentation, A/B testing, and test-and-learn frameworks. Develops and applies statistical and machine learning models to large datasets, translates analytical findings into business recommendations, and manages stakeholder communication. The role requires hands-on Python and ML expertise, strong methodological and research skills, problem-solving ability, and the capacity to balance technical delivery with business impact.

Design, build, and maintain Databricks-based data pipelines using PySpark and Delta Live Tables. Implement data models, optimize complex SQL and PySpark transformations, manage Unity Catalog for governance, support AI/ML downstream consumption, monitor and troubleshoot pipelines, contribute to CI/CD, and help power the semantic layer for Power BI dashboards.

Lead analytics and ML initiatives for Focus Category, building and deploying predictive models and AI-powered analytics platforms (NL-to-SQL, LLM features). Partner with product and business stakeholders to design experiments, craft dashboards, integrate data sources, and productionize ML/LLM pipelines and visualizations to drive product discovery, measurement, and growth.

Lead and optimize end-to-end chargeback operations, driving strategic planning, process improvements, and SLA management. Oversee cross-functional teams, integrate data science into dispute workflows, ensure compliance and risk controls, deliver client-facing reporting, and maintain SOPs/SOWs to improve efficiency, quality, and revenue recovery.

The role involves gathering business requirements, developing AI solutions, monitoring project progress, mentoring team members, and conducting market research in AI technologies.

The Technical Architect will lead service delivery for product implementation, guiding a technical team and ensuring project success with deep knowledge of the Blue Yonder platform and related technologies.

The role involves conducting AI research, teaching AI-related courses, collaborating on healthcare data modeling, and securing external funding, while mentoring students.

Develop and validate statistical programming code for clinical trial data analysis and reporting. Collaborate with data scientists and statisticians, ensure compliance with industry and regulatory standards, document programming processes and outputs, and review analysis datasets and final reports for accuracy and completeness.

Analyze claims, warranty, operations, and aftermarket data to develop decision-making tools, reporting automations, and self-service analytics. Build reports and datasets, identify quality and turnaround-time trends, manage customer master data, optimize data extraction, and automate processes. The role uses Python, SAP, Power BI, VBA, Excel, and Power Query, while supporting data projects and stakeholders across business teams.

Act as a hands-on solution architect for global accounts, demonstrating Snowflake, running demos and POCs, designing architectures, enabling customers via workshops and training, collaborating with sales/product/engineering, and driving adoption in BFSI accounts.