Top Data Scientist Jobs in Pune

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 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.

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.

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

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.

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

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 design and delivery of end-to-end AI/ML and Generative AI solutions, architect scalable cloud-based platforms, integrate LLM/RAG/agentic systems, implement MLOps and governance, and mentor teams while driving AI strategy with stakeholders.

Assess and test the bank's internal control framework across products and processes, investigate control weaknesses, produce reports and recommendations, lead/control assurance activities, engage stakeholders, and drive remediation and continuous improvement aligned to governance and regulatory requirements.

Lead the India-based Oncology Market Access & Insights (MAI) portfolio, driving delivery, prioritization and transformation (AI, workflow redesign). Act as primary liaison with global BU leadership, integrate primary research with analytics, enable cross-functional alignment, and manage a high-performing distributed team to deliver insight-driven access, pricing and launch solutions.

Lead transformation of Market Access & Insights in India, embedding AI and analytics, delivering payer insights, pricing/contracting strategy, policy and economic modelling. Build and scale in-house MAI capabilities and a high-performing team, govern portfolio prioritization, and align regional execution with global stakeholders.

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.

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.

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 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.

Develop, test, and operationalize data science and ML/LLM solutions using transactional and big data. Build pipelines, cloud functions, and microservice integrations; enable adoption and storytelling for business stakeholders.

Work on real-world data science projects using machine learning, statistics, experiments, and data exploration. Collaborate with stakeholders, produce actionable insights, validate analyses, participate in peer review, and iterate to improve products impacting millions of customers.

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.

Lead an AI-first transformation of procurement operations: assess Source-to-Pay maturity, design automation and LLM data governance, select and deploy S2P and CLM technologies, implement RPA/ML solutions, enable high-fidelity spend categorization and supplier compliance automation, and drive stakeholder integration and change management with IT and executive leadership.

Deliver live instruction, on-demand tutoring, video explanations, and QA reviews for Data Analytics learners. Mentor new experts, maintain QA standards (≥80%), contribute curriculum updates, and support learner engagement and academic quality improvements.