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.

Lead data science initiatives across product and business problems using analytics, experimentation, statistical inference, machine learning, and optimization. Define performance metrics, analyze large datasets, build dashboards, design experiments, and communicate actionable insights. Partner with product, engineering, and business teams while establishing data science practices and helping build Xometry’s India Product Development Center.

Lead AI and data science teams across enablement and experimentation initiatives. Set priorities, translate business needs into technical work, manage operating rhythms and resources, guide AI technology roadmaps, evaluate experiments, establish responsible AI governance, and optimize infrastructure and LLM-related costs. The role requires advanced Python, LLM and RAG expertise, machine learning frameworks, Azure deployment experience, statistical and forecasting knowledge, and ML lifecycle management skills.

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 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.

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

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

Leads data science and machine learning architecture engagements, analyzing diverse datasets and deploying models using cloud and big data technologies. Designs scalable, cost-effective solutions, including generative AI applications, and provides technical leadership across client delivery and presales activities. Creates standards, accelerators, reports, and presentations; mentors junior staff; and communicates technical concepts to business stakeholders.

The role combines hands-on software development with architectural leadership for Xometry’s AI-driven marketplace. Responsibilities include designing and implementing scalable services, solving complex technical problems, conducting code reviews, mentoring engineers, making technical decisions, and establishing engineering practices for the India PDT team. The Staff Data Scientist will collaborate with product, design, leadership, and global engineering teams while using AI development tools to improve delivery speed and maintain quality.

The Financial Services Leader will oversee the FS Consulting Practice, managing high-level strategy, client relationships, and operational delivery across several accounts while fostering innovation and cultural transformation.

The Engagement Manager will lead client workshops, drive analytics roadmaps, and liaise with clients to provide solutions in life sciences consulting.

The role requires applying data science principles to solve complex problems, contributing to the development and deployment of data science solutions, and provides insight based on data analysis.

The Data Scientist will analyze and model data for ML systems, focusing on Personalization and Recommendation Engines, and collaborate with teams to enhance data-driven solutions.

Lead data science initiatives for Quvia’s connectivity platform by developing predictive, prescriptive, forecasting, classification, and optimization models. Integrate anomaly detection, knowledge graphs, neural networks, and generative AI into products; deploy and continuously refine production-ready models; establish data quality and governance standards; document workflows; and mentor junior data scientists while collaborating cross-functionally with engineering and product teams.

Teach and develop intermediate-to-advanced courses in AI, ML, and Data Science using hands-on projects. Create syllabi and course materials, mentor students on projects, update curriculum with emerging tools (TensorFlow, Spark, cloud AI), participate in committees, recruitment, and professional development.

The Engagement Manager will drive execution of business-analytics roadmaps, lead workshops, define business problems, and manage client relations. This role combines hands-on contributions with team and customer management.

Develop and deploy machine learning, deep learning, graph AI, and generative AI systems for fraud detection, AML, transaction intelligence, and conversational AI. Responsibilities include feature engineering, model evaluation, GPU optimization, production deployment, scalable pipeline development, model monitoring, and collaboration with academic and cross-functional teams. The role involves LLMs, RAG, agentic workflows, graph neural networks, federated learning, and real-time financial systems.

Develop end-to-end AI systems for fraud detection, AML, anomaly detection, transaction intelligence, graph analytics, and conversational AI. Responsibilities include feature engineering, ML and deep learning model development, LLM and agentic applications, GPU optimization, scalable batch and real-time deployment, evaluation, monitoring, and collaboration with technical, academic, and business stakeholders.

Develop and deploy machine learning, deep learning, graph AI, and generative AI systems for fraud detection, AML, transaction intelligence, and conversational applications. Responsibilities include feature engineering, model evaluation, GPU/CUDA optimization, RAG and LLM development, federated AI exploration, production deployment, scalable pipeline design, and monitoring. The role involves working with transactional data, graph models, real-time systems, and cross-functional research and industry collaborators.

Develop and deploy machine learning, deep learning, graph AI, and generative AI systems for fraud detection, AML, transaction intelligence, and conversational applications. Responsibilities include feature engineering, model evaluation, GPU optimization, scalable batch and real-time deployment, monitoring, and production pipeline development. The role also involves LLM and agentic workflows, RAG, graph-based modeling, privacy-preserving AI, and collaboration with academic, technical, product, and business teams.

Lead the design and delivery of large-scale, production-grade AI and machine learning systems. Responsibilities span data engineering, feature design, model development, deployment, scaling, monitoring, and observability. The role requires hands-on expertise with machine learning, deep learning, cloud-native infrastructure, GPU computing, CI/CD, distributed data systems, federated AI, LLMs, and agentic AI, along with leadership and system design focused on latency and scalability.

Design and deliver end-to-end, production-grade AI and machine learning systems, including data engineering, feature design, model deployment, scaling, monitoring, and observability. The role combines hands-on data science expertise with technical leadership, covering deep learning, big data systems, CI/CD, Kubernetes, GPU computing, federated AI, LLMs, and agentic AI.