Top Data Scientist Jobs in Pune

Senior technical product owner responsible for defining and delivering scalable, secure platform capabilities. Own product vision and backlog, translate business use cases into technical requirements, prioritize trade-offs, partner with engineering/TPMs, drive operational excellence, and use runtime metrics to improve reliability and performance.

Design and implement technical features, collaborate with clients, write production-ready code, ensure quality deliverables, mentor team members, and implement ML models on large scale.

Lead the application of optimization techniques on large datasets, collaborate with engineering to deploy solutions, and manage project teams while contributing to data science capabilities and thought leadership.

Develop statistically effective algorithms and apply machine learning, deep learning, and generative AI techniques to high-dimensional problems. Use hypothesis testing, data mining, and big-data tools to identify trends, build predictive models and recommender systems, collaborate with clients and stakeholders, and evaluate emerging datasets and technologies for scalable analytical solutions.

Lead and grow a data science team to deliver predictive and ML-driven insights. Partner with business and engineering to build production-ready models, enforce model governance, ensure explainability, and measure business impact while driving continuous improvement.

The Advanced Data Science Associate Consultant will develop algorithms, execute statistical techniques, collaborate with clients, and integrate engineering principles into data science workflows.

The Data Science Manager will lead AI/ML solution development for clients in Supply Chain & Manufacturing, mentor data scientists, and drive business development efforts.

The Senior Product Manager will drive product strategy and development, collaborating with cross-functional teams to enhance customer experience, focusing on AI and SaaS solutions.

Drive innovation in e-commerce product management, collaborating across teams to define and execute product strategy, manage launches, and achieve business goals.

The role involves conducting AI research, teaching AI-related courses, collaborating on healthcare data modeling, and securing external funding, while mentoring students.

Lead development and execution of an enterprise B2B AI platform product (agentic systems). Define strategy and roadmaps, partner with Data Science and Engineering, prioritize customer and operational needs, use data and analytics to drive decisions, run rapid prototyping and experiments, remove delivery obstacles, mentor teams, and optimize scalable product journeys and workflows.

The Data Scientist will analyze and model data for ML systems, focusing on Personalization and Recommendation Engines, and collaborate with teams to enhance data-driven solutions.

As an Associate Product Manager, you will manage product development cycles, collaborate with engineering and operational teams, evaluate partnerships, and prioritize projects to improve customer adoption.

Develop and deliver pharmaceutical forecasts and analytical solutions using statistical and ML techniques. Collaborate cross-functionally to build assumptions, automate and improve forecasting processes, communicate findings to stakeholders, and maintain documentation and forecasting tools.

Evaluate AI-generated technical content, craft and answer advanced ML/AI prompts, assess model responses for accuracy, relevance, and clarity, identify errors or hallucinations, and provide feedback to improve AI systems.

Lead data science initiatives using advanced statistical techniques and machine learning to produce actionable insights. Partner with cross-functional teams to design data-driven solutions, manage and ensure integrity of large datasets, develop predictive models to forecast trends, and communicate results to stakeholders while staying current on emerging technologies.

The Principal Engineer, Data Science role involves leading data science projects and providing strategic insights using extensive IT experience.

The Senior Staff Engineer, Data Science role involves guiding team direction, solving complex challenges for clients, and leveraging technical expertise to innovate solutions.

The Principal Data Science Engineer will leverage statistical analysis and machine learning to optimize airline revenue performance and customer experience, collaborating with teams to develop data models and algorithms, and support product development cycles.

Lead AI and data innovation by developing ML models, optimizing data systems, and mentoring junior team members in a collaborative team environment.

Lead and manage data science engagements for insurance clients: provide technical direction across data engineering, modeling, visualization, Gen-AI/LLM solutions, MLOps, and reporting; run client sessions and project operations; recruit, coach, and manage offshore analytics teams to deliver client expectations.

Lead post-deployment operations and optimization of Agentic AI, LLM integrations, and Python-based analytical pipelines. Supervise a data science operations team, ensure secure high-availability production systems, manage integrations with enterprise systems (SAP, Salesforce, Postgres), and drive continuous improvement, monitoring, and adoption of analytics solutions.

The Facilitator will deliver academic support in Data Analytics through live sessions, tutoring, and quality assurance, while collaborating on curriculum enhancements and mentoring others.

Design, develop, and deploy enterprise AI/ML solutions including GenAI, LLM-based RAG systems, and agentic AI. Build scalable models, integrations, monitoring, and reusable frameworks while collaborating with stakeholders for production adoption and continuous improvement.

Lead design and delivery of NLP and Generative AI solutions, from problem definition through deployment. Build and fine-tune transformers and LLMs for summarization, QA, classification and chatbots, apply classical and deep learning methods, ensure scalable reproducible ML workflows in cloud environments, mentor junior data scientists, and collaborate with product and engineering to translate business problems into AI solutions.