Top Data Science Jobs in Bangalore

Lead and manage customer analysis, mentor analysts, conduct data preparation, apply data science methods, and communicate findings to clients.

Lead and manage customer analysis deep dives, execute strategies from leadership, communicate with clients, and mentor a team of analysts.

The role focuses on developing strategies for insights and analysis, managing analytics engagements, and guiding team development in data science.

The Lead Analyst leads customer analysis, manages a team of analysts, conducts data preparation, and provides insights using data science methods.

Lead and manage customer analysis, mentor a team of analysts, and apply data science methods to provide insights to clients.

Lead Analyst responsible for managing customer analysis, mentoring a team, applying data science methods, and delivering insights to clients.

The Lead Analyst leads customer analysis, manages a team of analysts, applies data science methods, and communicates insights to clients.

The Data Lead Consumer is responsible for defining and executing the data strategy, ensuring high-quality data delivery, governance, and alignment with business outcomes across the Consumer and Customer domains.

The role involves managing AI-powered supply chain solutions and delivering data-driven insights for operational improvements at KPMG.

Lead and manage customer analysis projects, mentor analysts, apply data science methods, and communicate insights to clients.

Lead the product management for data & analytics solutions, coordinating between business stakeholders and IT to deliver scalable, high-value solutions and ensuring ongoing enhancement and user adoption.

The IT Engineer Tech Lead will govern data acquisition architecture, establish reusable interfaces, define standards, and support data engineering teams across multiple sources, ensuring quality integration and analytics.

This role involves delivering data science solutions for clients, leading meetings, setting team objectives, and building client relationships to enhance performance and growth.

This role involves applying data science methods to provide insights, managing analytics engagements, and developing team strategies and training objectives.

The Associate Director of Data Science will lead the Data Science team at Lilly, managing projects, workflows, and talent development while collaborating with stakeholders to enhance data-driven decisions in pharmaceutical operations.

The Manager is responsible for developing strategies, managing analytics engagements, providing insights to clients, and leading the team.

Deliver analysis supporting client objectives through data science solutions; lead meetings, build relationships, and communicate insights for growth.

The role involves designing, building, and operating analytics pipelines on Google Cloud Platform, utilizing modern practices for orchestration, transformation, and machine learning integration.

Lead data science initiatives by defining roadmaps, managing teams, and engaging clients, while driving innovative AI and analytics solutions.

Lead the Data Science team for Lending at Grab, overseeing credit risk modeling, guiding ML strategies, and partnering with stakeholders for data-driven insights and model governance.

Drive strategic initiatives in healthcare analytics, manage consulting projects, deliver data-driven recommendations, and engage stakeholders across the Life Sciences industry.

Manage consulting projects in healthcare analytics, providing strategic advisory services, and developing data-driven recommendations to enhance business performance.

The Assistant Manager of Data Science Analytics at eBay will analyze seller performance, design A/B tests, create dashboards, and collaborate cross-functionally to support the seller journey and growth initiatives.

The role involves leading data science initiatives, optimizing processes, developing AI frameworks, and ensuring ethical compliance while engaging with users.

The Data Science Engineer III will develop and maintain scalable data pipelines, enhance data processing systems, and enable AI/ML use cases, collaborating with cross-functional teams and ensuring data quality.