Top Data Science Jobs in India

Lead product strategy and execution for Atlassian’s IT Operations and Incident Management offerings. Identify customer problems, prioritize opportunities, balance AI and deterministic capabilities, and partner with Engineering, Design, Data Science, Marketing, Sales, Support, and Customer Success. Improve incident detection and resolution, collaboration, adoption, retention, and business growth across enterprise and SMB customers.

Leads associate relations by investigating and resolving employment issues, complaints, conflicts, and policy concerns. Provides coaching and guidance to managers and associates, partners with Legal and other departments on compliance, analyzes case trends, recommends action plans, and improves associate experience processes. The role also develops HR capabilities, leads a team of consultants, escalates complex investigations, and supports cross-functional projects.

Own and execute AI product areas for Mastercard’s Payment Performance Intelligence platform, from discovery and technical requirements through launch and iteration. Partner with Data Science, Engineering, Platform, Commercial, Risk, Legal, Security, and Compliance teams to develop GenAI, chatbot, agentic service, predictive insight, and self-service analytics capabilities. Manage roadmaps, backlogs, prioritization, success metrics, stakeholder communications, and responsible AI requirements while mentoring junior product professionals.

Leads Pfizer’s India Integrated Insights & Strategy function, advising commercial executives through analytics, market research, forecasting, and data-driven brand and portfolio strategy. Owns the analytics roadmap, budget, performance measurement, PMR strategy, vendor partnerships, and resource allocation. Oversees advanced analytics, AI/ML, customer segmentation, targeting, field-force sizing, and launch planning while developing a high-performing team and coordinating with regional and global GCA stakeholders.

Leads planning and execution of complex data science projects from business understanding through model deployment and monitoring. Coordinates product, engineering, data science, and leadership teams; manages roadmaps, dependencies, risks, trade-offs, escalations, and status reporting. Translates technical concepts into business language, drives continuous improvement, facilitates retrospectives, and ensures alignment across matrixed teams.

Leads enterprise AI and machine learning initiatives by translating business problems into scalable solutions. Responsibilities include hands-on experimentation, exploratory and statistical analysis, feature engineering, Generative AI and agentic AI development, RAG architecture, proof-of-concept delivery, data visualization, documentation, and collaboration with business, product, architecture, engineering, and leadership teams to accelerate production adoption.

Leads data science initiatives using statistical modeling, machine learning, NLP, OCR, and generative AI technologies. Responsibilities include feature engineering, hypothesis testing, model development, stakeholder engagement, analytical storytelling, structured problem solving, and people leadership focused on talent development and team engagement.

Seeking senior software engineers with proven expertise in at least one technical area, including AI/ML, LLMs, data, full-stack or backend development, automation, cloud infrastructure, and DevOps. Responsibilities include solving complex problems independently, collaborating across technologies, and delivering production-quality projects in a startup environment. Engagements may be project-based or ongoing.

The VP of Data Science will define and execute the company’s AI strategy, transform cybersecurity products into AI-first platforms, and lead development of production-grade machine learning capabilities. Responsibilities include building data science teams, establishing MLOps and AI architecture standards, deploying autonomous and LLM-driven workflows, governing responsible AI use, partnering across Product and Engineering, and representing the company through client engagements, research, publications, and industry thought leadership.

Faculty positions in Computer Engineering, Computer Science, Information Technology, Artificial Intelligence, Data Science, and Business Analytics. Responsibilities include teaching, conducting research, publishing in peer-reviewed journals, guiding doctoral candidates, and pursuing consultancy or patent work. Professor and Associate Professor applicants need a Ph.D. or equivalent professional experience, substantial teaching, research, or industry experience, publications, and research scores. Assistant Professors need a first-class master's degree, with Ph.D., experience, and publications preferred.

Oversee organizational data science strategy, lead high-impact initiatives, align data science practices with organizational goals, and provide executive-level strategic guidance. The role requires a bachelor's or master's degree in data science, computer science, statistics, or a related field, along with 8–12 years of data science or data analysis experience.

Leads multidisciplinary teams building and scaling agentic AI products for customer-facing and enterprise use cases. Responsibilities include AI product strategy, roadmaps, portfolio prioritization, hiring and talent development, stakeholder management, Agile delivery, governance, metrics, risk management, and production reliability. The role requires expertise in LLM systems, data science, machine learning, software engineering, distributed systems, APIs, microservices, and AI product lifecycle management.

Refine and develop age-appropriate AI and coding curriculum for K-12 learners. Responsibilities include improving lesson plans, assignments, quizzes, code examples, and activities; simplifying complex concepts; identifying student learning gaps; collaborating with teachers; updating content on AI and coding trends; and creating projects using tools such as ChatGPT, Hugging Face, and scikit-learn.

Leads strategy, roadmap, lifecycle management, and go-to-market activities for AI/ML-powered products and platform integrations. Partners with Data Science, Engineering, Legal, Compliance, Operations, and Sales to define use cases, evaluation criteria, data requirements, responsible AI practices, success metrics, and post-launch performance monitoring. Requires substantial experience managing AI/ML or data-driven products and familiarity with GenAI and LLM technologies.

Lead product discovery, strategy, solutioning, and zero-to-one development for agriculture-focused data and AI products. Partner with engineering, design, data science, sales, and customer success teams to validate customer problems, define roadmaps, launch scalable solutions, and drive adoption. The role requires strong agriculture expertise, commercial judgment, analytical skills, and experience delivering complex products in a startup environment. Knowledge of remote sensing and geospatial technologies is advantageous.

Leads data science, AI, and machine learning capabilities across enterprise platforms. Develops cloud-based content classification systems, governs content lifecycle and metadata enrichment, embeds AI intelligence into product roadmaps, and creates agentic AI frameworks. Partners with product, engineering, and domain teams to align technical execution with business goals while mentoring operations and enablement teams.

Leads medium-sized data science projects, establishes standards and documentation, mentors junior team members, and collaborates across departments to align data science practices.

Develop and deploy production-grade ML/AI solutions using Python and Azure ML. Build, monitor, retrain, and optimize models (regression, forecasting, classification), collaborate with DevOps/MLOps, and maintain production data science pipelines and environments.

Develops production-ready AI and machine learning applications using cloud AI services and generative AI models. Responsibilities include applying deep learning, neural networks, chatbots, and image processing; collaborating across teams; making technical decisions; mentoring junior engineers; serving as a subject matter expert; and improving processes. The role requires strong data science expertise, knowledge of machine learning algorithms, cloud deployment strategies, programming in Python or R, and data visualization skills.

Leads the design, execution, and sale of healthcare analytics solutions for payer and provider clients. Applies expertise across cloud data platforms, integration, visualization, AI, machine learning, data science, modeling, management, and governance. Ramps up analytics programs, aligns work across teams, communicates directly with clients, develops selling strategies, and supports consultative sales. Requires deep knowledge of the US healthcare value chain and experience solving organizational problems with data and analytics.

Lead end-to-end product strategy and delivery for AI-driven work intake and platform capabilities. Conduct customer discovery, validate solutions, partner with engineering, UX, AI/ML, and analytics teams, and ship features that improve intake efficiency and experience for admins and end users. Pivot across domains as business priorities evolve and measure business impact for millions of users.

Develop and lead production-ready AI and machine learning applications using cloud AI services, generative AI, deep learning, neural networks, chatbots, and image processing. Responsibilities include designing ML strategies, deploying models, preprocessing large datasets, feature engineering, optimizing neural networks, coordinating cross-functional teams, making technical decisions, and mentoring junior engineers.

Design and develop production-ready AI and machine learning applications using cloud AI services and on-premises pipelines. Apply generative AI, deep learning, neural networks, chatbots, and image processing techniques. Lead AI strategy and implementation, collaborate across teams, make technical decisions, solve complex problems, and mentor junior engineers. Perform data preprocessing, feature engineering, model optimization, and deployment of AI solutions in production environments.

Lead AI-first product strategy and execution for enterprise SaaS products. Own prioritization, backlogs, business cases, customer insights, experimentation, success metrics, and cross-product dependencies. Partner with engineering, data science, and Scrum teams to deliver AI-enabled capabilities, including automation, predictive intelligence, and generative experiences. Ensure responsible AI governance, measurable customer outcomes, and platform scalability while mentoring junior product professionals.

Lead enterprise production GenAI and machine learning initiatives for financial crime, risk, and compliance. Own the full model lifecycle, including data controls, feature engineering, validation, explainability, monitoring, retraining, and model risk governance. Build MLOps infrastructure and partner with compliance, investigations, operations, technology, audit, and senior leadership to develop defensible detection and decisioning solutions. Mentor junior data scientists and drive applied research in anomaly detection, graph analytics, weak supervision, and related methods.