Top Data Science Jobs in India

The Enterprise Account Executive will strategize, prospect, and close business in the Southern India market, focusing on client needs and driving Snowflake adoption.

The Scrum Master will lead Data Science teams, facilitating Scrum events, coaching team members, and ensuring delivery performance while promoting Agile practices in a research-driven environment.

The Data Science Manager leads a team to develop methods for analyzing big data, oversees software development for data processing, and mentors team members.

The Data Scientist applies data science principles to solve diverse problems, manages projects, and leads research with limited complexity. They analyze data to provide innovative solutions.

The Engineering Manager will lead AI & ML initiatives focused on data collection, overseeing technical projects and developing a high-performing team. Responsibilities include providing technical direction, mentoring staff, ensuring system reliability, and maintaining data integrity and security. The role requires expertise in NLP and data processing with an emphasis on collaborative leadership and continuous innovation.

The Senior Data Scientist will enhance the predictive modeling capabilities of Lyric's platform, focusing on machine learning and statistical methods for supply chain optimization.

The User Researcher will lead research projects from discovery to synthesis, using various methods to gain insights that improve products while collaborating with stakeholders.

Lead a team of engineers in developing financial services, collaborating with stakeholders to build effective systems, and fostering a culture of innovation and excellence.

The Data Scientist will develop enterprise solutions, implement data strategies, create financial analytics workflows, and employ machine learning for data management and analysis. Responsibilities include building POCs, working with cross-functional teams, and handling data processing and automation tasks.

Lead value measurement for the Experience Data Platform: define KPIs, build executive dashboards for cost/performance/adoption, apply ROI frameworks, run complex analyses, ensure data quality/governance, partner across Product/Engineering/Finance, and lead a team to translate insights into strategic decisions.

The role involves data analysis and machine learning in manufacturing, utilizing various data techniques, software development, and strong communication skills.

The role involves developing smart manufacturing applications, deploying data science strategies, providing technical consulting, and analyzing large datasets using machine learning.

Lead design, develop, and maintain scalable Python backend applications and data pipelines. Mentor engineers, conduct code reviews, troubleshoot performance issues, participate in architecture and full SDLC, and improve engineering processes.

The role involves providing strategic advisory services, managing business transformations, developing AI models, and collaborating with cross-functional teams to implement data-driven solutions.

Lead AI/ML teams in developing and deploying scalable AI systems for educational solutions. Manage projects, mentor engineers, and collaborate across teams.

Lead the development and deployment of AI/ML solutions, managing the entire model lifecycle and ensuring tangible business outcomes across various projects.

Lead and grow Gracenote's AI & Data Science teams, drive AI/NLP/Computer Vision/MLOps initiatives, deploy production ML/GenAI solutions, enforce quality and lifecycle processes, partner cross-functionally, and advance predictive analytics and research for media audience measurement.

The Lead Pega Decisioning Architect is responsible for designing and implementing Pega solutions, managing decisioning components, and collaborating with teams to ensure delivery within deadlines, leveraging expertise in Pega systems and frameworks.

Lead the design and implementation of Pega Decisioning solutions, manage technical deliveries, collaborate with teams, and create analytical models.

The Support Engineer will optimize machine learning techniques, conduct data mining, handle forecasting issues, and enhance datasets while collaborating closely with engineering and product teams.

The NPDE AI/ML Leader will define and implement strategic AI solutions to enhance product development in semiconductor processes, working closely with domain experts and managing software teams.

Lead classic ML data science efforts: design and validate statistical analyses and models, build forecasting and classification solutions, use ML frameworks and MLOps tools to productionize and monitor models, and guide an AI/ML engineering team.

The role involves leading AI and ML strategies, integrating advanced capabilities into products, and optimizing data science models, emphasizing innovation and reliability.

The Senior Data Scientist will develop machine learning models, collaborate with stakeholders, analyze data sets, and stay updated on AI advancements.

Assist in data science algorithm development, collaborate on A/B tests, and analyse datasets to provide actionable insights for strategic initiatives.