Lead Data Scientist - AI Delivery

Overview

Lead the end-to-end delivery of multiple AI and Data Science initiatives as the India delivery partner to the U.S. AI leadership team, working on the Digital Twin platform.

What you'll do

• Own end-to-end delivery of multiple AI and Data Science initiatives from requirements and solution design through development, testing, deployment and production stabilization.
• Act as the primary India delivery lead for assigned initiatives and independently manage priorities, milestones, dependencies, risks and execution.
• Lead and mentor a small team of Data Scientists, AI Engineers and technical resources, providing clear direction, technical guidance and accountability.
• Translate ambiguous business problems into practical AI, analytics and engineering solutions aligned to business outcomes.
• Partner closely with the Digital Twin platform team to resolve environment, integration, deployment, performance and production issues.
• Provide sufficient technical depth to distinguish whether issues originate in the model, application, integration, data or platform layer and coordinate the appropriate resolution.
• Participate in solution architecture and design discussions and ensure solutions are scalable, reliable, secure and maintainable.
• Guide teams on API integrations, data pipelines, authentication, cloud environments, CI/CD, logging, monitoring and other production dependencies.
• Provide technical oversight for Machine Learning, NLP, document intelligence, Generative AI, RAG and Agentic AI solutions.
• Review model and AI approaches, evaluation methodology, error analysis, performance metrics and implementation trade-offs.
• Partner with the AI Modeling team on model development, validation, monitoring, drift detection and productionization when required.
• Drive root-cause analysis and corrective actions when solution accuracy, reliability or production performance is below expectations.
• Promote reusable frameworks, components and delivery patterns that can support multiple products and customers.
• Partner with Product, Business, Advisory, Engineering and Content teams to ensure technical delivery remains aligned to customer and business needs.
• Provide concise status updates covering progress, risks, dependencies and decisions required, and escalate issues early with recommended actions.
• Present technical solutions, delivery updates and recommendations clearly to senior leaders and non-technical stakeholders.
• Support hiring, interviewing, mentoring and capability development as the AI/Data Science team grows.

What you'll need

• 8+ years of progressive experience in Data Science, Machine Learning, Applied AI, AI Engineering or related technical roles.
• Demonstrated experience leading end-to-end delivery of production AI, Machine Learning or analytics solutions.
• At least 2-3 years of experience leading technical teams, workstreams or complex cross-functional initiatives.
• Proven ability to independently manage multiple projects or workstreams with limited supervision.
• Experience working with Engineering, Platform, DevOps or Cloud teams to move AI solutions into production.
• Experience troubleshooting production issues and coordinating resolution across multiple technical teams.
• Experience managing, mentoring or providing technical direction to Data Scientists and/or AI Engineers.
• Experience communicating technical decisions, risks and outcomes to senior stakeholders.
• Strong understanding of Statistics, Machine Learning and predictive modeling concepts.
• Experience with regression, classification, tree-based models, ensemble methods, clustering and other common Machine Learning techniques.
• Strong understanding of model evaluation, validation, feature engineering, overfitting, class imbalance, data leakage and model monitoring.
• Ability to challenge model selection and evaluation approaches and assess trade-offs across accuracy, explainability, stability and maintainability.
• Understanding of model lifecycle management including development, validation, deployment, monitoring, drift detection and retraining.
• Ability to determine when traditional Machine Learning, Generative AI or a hybrid approach is most appropriate.
• Strong Python skills and good SQL and data engineering fundamentals.
• Strong understanding of REST APIs and enterprise system integrations.
• Experience with Azure and/or AWS cloud environments.
• Good understanding of Docker, Git, CI/CD and modern software delivery practices.
• Working knowledge of application architecture, microservices and scalable system design.
• Understanding of authentication, secrets management, API security and enterprise networking concepts.
• Experience with logging, monitoring, troubleshooting and production support.
• Understanding of MLOps and/or LLMOps practices including versioning, deployment, monitoring and evaluation.
• Ability to work effectively with platform engineers without needing to be a dedicated platform engineer.
• Bachelor's or Master's degree in Statistics, Mathematics, Data Science, Computer Science, Artificial Intelligence, Engineering or another quantitative/technical discipline.

Nice to have

• Experience working in distributed or global teams.
• Experience in Financial Services, Banking, Insurance, Risk, Regulatory or Compliance environments.
• Experience with document intelligence, NLP or unstructured data processing.
• Experience with Generative AI, LLMs, embeddings, RAG and Agentic AI.

Details

• Location: Pune, India (Smartworks).
• Applicants may be required to appear onsite at a Wolters Kluwer office as part of the recruitment process.
• Interviews must be conducted without AI tools or external prompts; virtual backgrounds must be removed.