Top Data Science Jobs in India

Develop industry-relevant technical curricula, learning materials, labs, assessments, coding exercises, and projects. Teach classes, workshops, bootcamps, and live coding sessions; record tutorials and demonstrations; research emerging technologies; review content quality; and support trainer development. The role requires hands-on expertise in Java and DSA and/or Python, knowledge of AI and data technologies, curriculum development experience, strong communication skills, and comfort with classroom and video delivery.

Develop advanced data science algorithms, apply statistical and machine learning techniques to large datasets, evaluate emerging data technologies, and build scalable generative AI and deep learning solutions. Collaborate with clients and engineering teams to communicate findings, deploy models into production, and maintain reliable workflows using software engineering best practices.

Lead advanced data science and numerical optimization projects using large, unstructured datasets. Develop computationally efficient analytic solutions, collaborate with engineering teams on deployment, evaluate emerging technologies, build scalable analytic assets, communicate findings to clients, contribute to research and publications, and manage project teams.

Lead and execute Codingal's curriculum roadmap across Coding, Math, and ELA. Manage a curriculum team and external SMEs, ensure lesson and assessment quality, strengthen teacher training and evaluation, integrate AI into learning experiences, and collaborate with product, engineering, and business teams to improve student engagement and retention. Build scalable curriculum systems, feedback loops, and launch processes to drive measurable learning outcomes and operational consistency.

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.

Lead and coach cross-functional, global teams to deliver projects using Agile/Hybrid practices. Manage program portfolio, budgets, QDC metrics, risk, and stakeholder communication. Drive Agile transformation, DevSecOps and AI integration, optimize processes, and report progress to leadership.

Senior Solution Engineer responsible for presenting Snowflake’s data platform, building demonstrations and proofs of concept, advising customers on data architecture and implementation, and supporting adoption across global accounts and GCCs. The role partners with sales, product, engineering, and marketing; delivers workshops and training; develops technical champions; and communicates Snowflake’s value to technical and executive audiences. Requires extensive customer-facing solution engineering experience, modern data stack expertise, and hands-on SQL and Python skills.

Leads Moderna’s Analytical Development organization and establishes enterprise strategy, technical standards, governance, resourcing, and talent development. Oversees analytical method development, product characterization, microbiology, bioassays, control strategies, technical transfers, regulatory submissions, lifecycle management, investigations, and inspection readiness for mRNA and advanced therapeutics. Partners across CMC, manufacturing, quality, regulatory, supply chain, and external partners while advancing analytical innovation, automation, data science, and AI-enabled workflows.

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.

Manage and evaluate scientific manuscript submissions in computer science, electrical engineering, data science, machine learning, and related fields. Coordinate peer review, provide author support, recruit research papers through scientific outreach, attend conferences and academic visits, and help strengthen journal visibility. The editor will also contribute to journal strategy, science communication initiatives, and collaborative editorial planning.

Analyzes air cargo industry trends, business processes, and operational needs to identify solution opportunities. Translates requirements into functional specifications and user stories, facilitates stakeholder workshops, validates solution designs, and supports proofs of concept. Collaborates with sales, architects, product managers, and delivery teams on proposals, RFPs, business cases, pricing, ROI, and market positioning. Provides air cargo domain advisory across freight forwarding, customs, handling, and last-mile logistics.

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.

Develop machine learning, generative AI, and LLM solutions, including propensity, classification, regression, clustering, fine-tuning, evaluation, and AI-agent workflows. Build secure, explainable, privacy-preserving models while applying responsible AI, governance, and regulatory requirements. Support MLOps activities such as deployment, monitoring, versioning, CI/CD, and reproducible pipelines. Collaborate with product, engineering, analytics, and compliance teams across structured, unstructured, and potentially geospatial data projects.

Develop machine learning and generative AI models, including propensity, classification, regression, clustering, LLM, and agentic workflow solutions. Responsibilities include data preparation, fine-tuning, evaluation, optimization, guardrails, explainability, privacy, responsible AI compliance, model risk mitigation, and MLOps deployment and monitoring. The role collaborates with product, engineering, analytics, and compliance teams to create secure, scalable, and explainable AI systems.

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

Performs strategic customer, marketing, and sales analytics to identify acquisition and revenue opportunities. Translates complex data into recommendations for leaders and cross-functional partners, using storytelling and thought leadership to influence decisions. Applies machine learning, data science, and emerging generative AI capabilities to commercial card business problems while collaborating across business, technology, and vendor teams.

Develop and evaluate machine learning and AI solutions, experiment with AI agents and agentic workflows, and help deploy AI-powered tools. Assess model quality, reliability, and user experience while collaborating with engineers, product teams, and subject matter experts. Present findings to stakeholders and gain hands-on experience with modern AI technologies and cloud platforms.

Leads data science and analytics for performance marketing, including machine learning, experimentation, campaign optimization, marketing attribution, customer segmentation, KPI development, dashboards, and reporting automation. Translates business challenges into AI-enabled analytics products, defines requirements, collaborates with engineering, and communicates actionable insights to stakeholders.

Develop analytics, experimentation strategies, and data-driven insights for AI-powered selling experiences. Design and analyze A/B tests, define product metrics, study seller behavior, build scalable analytics pipelines and dashboards, and partner with Product and Engineering to improve listing flows, seller activation, and marketplace growth. Translate complex analyses into actionable recommendations while ensuring data quality, tracking integrity, and reliable measurement.

Develop data-driven analytics products using large datasets, Python, SQL, statistics, and machine learning. Responsibilities include exploratory analysis, feature engineering, predictive modeling, validation, benchmarking, automated workflow development, UAT support, productization, documentation, and model governance. The role collaborates with product, technology, and business teams to deliver scalable, interpretable, and reliable decision-support solutions.

Develop data-driven risk and analytics products using bureau and financial services data. Responsibilities include exploratory analysis, feature engineering, statistical and machine learning model development, validation, benchmarking, workflow automation, UAT support, productization, documentation, and collaboration with Product, Technology, and business teams. The role requires strong Python and SQL skills, reproducible coding practices, model governance, and the ability to communicate analytical insights to technical and non-technical stakeholders.

Develops commercial bureau-based credit risk models, scorecards, segmentation frameworks, and portfolio analytics for MSME and commercial lenders. Responsibilities include analyzing lending data, engineering features, validating predictive models, assessing portfolio performance, creating scalable Python and SQL datasets, and delivering client-focused analytics and proofs of concept. The role also involves stakeholder collaboration, model governance, documentation, compliance, and supporting productization and pre-sales initiatives.