Top Data Science Jobs in India

Lead product strategy and roadmap development for ServiceNow’s AI Agent Platform. Prioritize features, create technical AI proofs of concept, collaborate with engineering, design, and AI research teams, and demonstrate solutions to customers and internal stakeholders. Translate customer requirements into functional solutions, define KPIs, track outcomes, and communicate recommendations on emerging AI and platform technologies.

Leads end-to-end AI, machine learning, generative AI, and data science programs from ideation through production. Develops roadmaps, governance frameworks, delivery metrics, and executive reporting while coordinating engineering, product, data science, UX, and business teams. Manages risks, dependencies, resources, and stakeholder alignment; drives Agile practices, release planning, PI Planning, and business value measurement. Mentors project and program managers and ensures responsible AI, compliance, security, and model lifecycle practices.

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 Micron’s global supply chain innovation portfolio as a technical authority. Defines technology strategy, evaluates emerging capabilities, and guides AI, automation, analytics, digital twin, and optimization initiatives from research through pilots and operational adoption. Advises executives on investments, builds external and internal innovation networks, resolves complex cross-domain problems, and mentors technical talent. Requires a sustained record of globally impactful innovation, measurable ROI, and influence across functions, sites, and geographies.

Analyzes business and operational data using statistical modeling, predictive analytics, machine learning, and programming techniques. Defines hypotheses, gathers and validates structured and unstructured data, identifies trends and patterns, and develops actionable recommendations. Collaborates with stakeholders to influence business strategy, communicate insights, and solve moderately complex problems, primarily within financial services.

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.

Develop statistically effective algorithms and apply machine learning, hypothesis testing, data mining, and retrieval techniques to analyze complex datasets. Identify trends, create visualizations, communicate findings to clients and stakeholders, and evaluate emerging datasets and technologies. The role requires collaboration across consulting and analytics teams, with opportunities to build scalable solutions using generative AI, deep learning, big data, and cloud technologies.

Develop and validate machine learning and statistical models within Capital One’s Model Risk Management team. Build data products, analytical tools, and self-service capabilities using large, complex datasets. Partner with data scientists, quants, engineers, risk managers, auditors, and regulators to assess model performance, ensure regulatory compliance, and influence business strategies. Communicate technical findings to technical and nontechnical stakeholders while researching emerging technologies such as generative AI.

Develop and deploy Python-based AI and data science solutions for global research modernization. Responsibilities include machine learning model development, validation, workflow automation using LLMs, data exploration, stakeholder collaboration, data governance, and communicating analytical insights. The role supports solutions from problem framing through production deployment while ensuring regulatory, ethical, and model governance compliance.

Develop applied data science, machine learning, generative AI, and agentic AI solutions for automated manufacturing environments. Responsibilities include data extraction and transformation, feature engineering, statistical modeling, anomaly detection, distributed processing, ML application development, and integration with Snowflake and Azure platforms. The role requires production-quality software, analytical problem-solving, and communication of technical findings to technical and business stakeholders.

Designs, develops, tests, delivers, and operates scalable backend Java services for PayPal’s real-time fraud detection and payments risk systems. Builds REST APIs and microservices, improves reliability and performance, contributes to automated testing and continuous integration, participates in code reviews, guides junior engineers, collaborates cross-functionally, and helps shape technical direction while maintaining highly available services.

Develop fraud detection and monitoring solutions using machine learning, statistical modeling, exploratory data analysis, and large-scale datasets. Build dashboards, identify fraud patterns, recommend detection rules, support data mart design, and deliver reporting and documentation. Collaborate with engineering, product, risk, analytics, business, and global technology stakeholders to implement technical solutions that reduce fraud and improve customer outcomes.

Design and architect high-scale, geo-distributed web platforms supporting Microsoft Office applications. Lead performance benchmarking, scalability, reliability, modernization, and secure software development. Build and test features, develop proofs of concept, define components, and collaborate across engineering teams. Identify AI productivity opportunities and promote engineering excellence through technical leadership, communication, and influence.

Analyze marketing and sales performance, customer trends, and growth opportunities for the US commercial card business. Translate complex data into strategic recommendations, collaborate with cross-functional stakeholders, communicate insights to senior leaders, and apply machine learning and generative AI—including RAG, embeddings, vector search, and agentic workflows—to develop innovative analytics solutions.

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.

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.

Leads and develops a Data Science team, oversees predictive and machine learning model delivery, establishes governance and lifecycle standards, and partners with business, engineering, and analytics leaders. Translates complex data into executive insights, frames ambiguous business problems, improves analytical processes, ensures production-ready models, and measures business impact while developing technical talent and promoting responsible, explainable analytics.

Leads business strategy and operations for Pipeline and Project Management and Data Science within R&D. Serves as Chief of Staff to executive leaders, overseeing leadership cadence, workforce planning, capacity modeling, budgets, financial forecasting, due diligence, integrations, OKRs, dashboards, communications, process improvement, change management, and AI-enabled operational transformation.

Lead strategic sales and account growth across GCC and Fortune 500 organizations. Develop account strategies, identify new business and expansion opportunities, manage complex enterprise sales cycles, engage senior stakeholders, and position Data, Analytics, AI, and digital transformation solutions. Partner with consulting, delivery, capability, and leadership teams to create solutions, close high-value opportunities, and drive long-term revenue growth and customer success.

Drive end-to-end B2B sales and go-to-market activities for data science and applied AI solutions. Responsibilities include prospecting, customer relationship management, technical presentations, solution scoping, proposal development, business analysis, and resolving technical objections. The role partners with solution architects and data science teams to develop AI and cloud solutions, demonstrate Google Cloud integrations, manage client expectations, and identify new business opportunities across multiple industries. Travel to customer sites and events may be required.

Leads engineering and data science teams building AI/ML data collection systems for large-scale unstructured data. Provides technical direction across NLP, classifiers, LLMs, MLOps, data pipelines, cloud platforms, security, and system operationalization. Responsibilities include mentoring and hiring talent, overseeing scalable model and pipeline development, collaborating cross-functionally, improving delivery processes, and ensuring reliable, secure, high-performance solutions.

Leads the design, development, validation, monitoring, and production deployment of AI and machine learning solutions. Provides technical direction and mentorship to data scientists and engineers, translates business needs into scalable architectures, builds Python-based ML pipelines, resolves complex technical issues, and drives high-quality hands-on delivery across the full model lifecycle.

Develops and operationalizes AI-enabled vulnerability management capabilities across application and infrastructure security within a DevSecOps framework. Responsibilities include security engineering, penetration testing, AI system assessments, threat and vulnerability analysis, incident management, adversarial testing, and mitigation of AI-driven risks. The role partners with engineering, operations, cloud security, incident response, compliance, and risk teams to deliver production-ready cybersecurity solutions and provide strategic technical guidance.

Leads multiple teams delivering enterprise AI, machine learning, data science, optimization, and agentic AI products. Establishes vision, roadmaps, architecture, reliability, governance, and performance practices while overseeing portfolio delivery and measurable business outcomes. Partners with executives and cross-functional leaders, recruits and develops talent, drives operational excellence, and builds scalable AI capabilities across the enterprise.

Leads engineering for supplier and buyer enterprise SaaS journeys, overseeing engineering organizations, delivery, architecture, quality, production ownership, hiring, budgets, and global teams. Partners with product, design, AI, data, and technology leaders to deliver integration-heavy workflows, AI-enabled agents, predictable releases, secure development, and measurable improvements in reliability and engineering performance.