About the Role

The FCC DnA team is seeking an experienced Lead Data Scientist to provide technical and delivery leadership across multiple AI and Data Science initiatives. This role will serve as a key India delivery partner to the U.S. AI leadership team and will be responsible for independently driving execution across products and use cases such as Data Integrity, intelligent document processing, Broker Statement processing, workflow automation and other Applied AI initiatives.

The role requires a strong technical leader who can manage a small team, translate business priorities into executable plans, remove delivery blockers and work effectively across Data Science, Product, Engineering and Platform teams. Since these solutions are deployed on the Digital Twin platform, the successful candidate should be comfortable working with platform engineering teams on APIs, environments, deployments, integrations, monitoring and production issues.

This is not intended to be a pure people-management role or a pure modeling role. The person should maintain strong technical depth across Data Science, Machine Learning and Applied AI, while also understanding enough software and platform engineering to move solutions from development through production. Experience with Statistical Modeling and model lifecycle management is a strong plus, as the role will also support and collaborate with the broader AI Modeling team.

The successful candidate should be able to work independently with limited day-to-day supervision, take ownership of delivery outcomes, communicate risks and decisions clearly, and present technical progress and recommendations confidently to senior leaders.

Key Responsibilities

·  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.

Required Qualifications

Experience

·  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.

·  Experience working in distributed or global teams is strongly preferred.

·  Experience in Financial Services, Banking, Insurance, Risk, Regulatory or Compliance environments is desirable.

Data Science & Modeling Expertise

·  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.

·  Experience with document intelligence, NLP or unstructured data processing is highly desirable.

·  Experience with Generative AI, LLMs, embeddings, RAG and Agentic AI is highly desirable.

·  Ability to determine when traditional Machine Learning, Generative AI or a hybrid approach is most appropriate.

AI, Engineering & Platform Skills

·  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.

Leadership Mindset

·  Takes ownership of outcomes rather than limiting responsibility to assigned tasks.

·  Works independently and moves initiatives forward even when requirements or dependencies are ambiguous.

·  Balances business priorities, technical quality, delivery speed and long-term maintainability.

·  Identifies risks early, performs root-cause analysis and brings solution-oriented recommendations.

·  Challenges technical assumptions constructively and raises the quality of decisions across the team.

·  Develops team members through technical coaching, clear expectations and meaningful feedback.

·  Communicates confidently with Data Scientists, Engineers, Product leaders and senior business stakeholders.

·  Can present complex technical topics in clear business language and influence decisions across teams.

·  Remains hands-on enough to provide meaningful technical guidance while successfully leading delivery across multiple initiatives.

·  Demonstrates strong judgment, accountability and a collaborative leadership style.

Education

Bachelor's or Master's degree in Statistics, Mathematics, Data Science, Computer Science, Artificial Intelligence, Engineering or another quantitative/technical discipline.

Our Interview Practices

To maintain a fair and genuine hiring process, we kindly ask that all candidates participate in interviews without the assistance of AI tools or external prompts. Our interview process is designed to assess your individual skills, experiences, and communication style. We value authenticity and want to ensure we’re getting to know you—not a digital assistant. To help maintain this integrity, we ask to remove virtual backgrounds and include in-person interviews in our hiring process. Please note that use of AI-generated responses or third-party support during interviews will be grounds for disqualification from the recruitment process.

Applicants may be required to appear onsite at a Wolters Kluwer office as part of the recruitment process.