Senior Data Scientist

, India Job Family Group: Research and Development Worker Type: Regular Posting Start Date: July 30, 2026 Business Unit: Finance Experience Level: Experienced Professionals Job Description: What’s the role This is a senior individual contributor (IC) role operating as the technical leader for AI and Data Science engineering within the NFR Data Science team. The role is responsible for driving technical excellence, establishing best practices, defining solution architectures, and elevating the team's capability in building, deploying, and scaling enterprise-grade AI solutions. The successful candidate will serve as the team's subject matter expert for Databricks-based AI platforms, MLOps, Agentic AI development, and Databricks Genie solutions. The role is deliberately self-driven and self-defined. The incumbent is expected to proactively identify opportunities, create a pipeline of impactful work, influence without formal authority, and continuously raise the technical maturity of the team through innovation, coaching, standards, and strategic guidance What you will be doing AI Engineering Leadership & Best Practices Act as the technical authority for AI Engineering, MLOps, Databricks Genie development, and enterprise AI solution design. Define, implement, and continuously improve AI engineering standards, coding practices, review processes, deployment frameworks, and technical governance. Establish best practices covering: AI architecture design Software engineering standards Testing frameworks Deployment automation Model lifecycle management Documentation standards Production support models Drive adoption of reusable frameworks, accelerators, templates, and reference implementations. Conduct architecture reviews and provide technical guidance across AI and data science initiatives. Databricks Genie & Agentic AI Leadership Design, develop, and deploy enterprise-grade Databricks Genie solutions for business stakeholders across markets. Own the reference architecture for Genie-based solutions. Define best practices for: Semantic model design Prompt engineering Response evaluation Accuracy optimization Cost optimization Security and governance Lead the adoption of Agentic AI capabilities including: Multi-agent systems AI workflow orchestration Tool usage patterns Retrieval-augmented generation (RAG) Human-in-the-loop architectures Establish scalable development patterns that enable faster and more consistent delivery of GenAI solutions. MLOps & Solution Engineering Lead implementation and continuous improvement of MLOps practices across the team. Define standards for: Source code management, CI/CD pipelines Automated testing, Model versioning Monitoring and observability, Production deployment Ensure solutions are designed for maintainability, reliability, scalability, and operational excellence. Drive adoption of engineering best practices using SOLID principles, object-oriented design, and modern software development practices. Innovation & Technical Strategy Continuously benchmark external industry advancements and identify opportunities for business adoption. Lead rapid prototyping, proof of concepts, and experimentation activities. Build and maintain a technology innovation backlog aligned to strategic priorities. Evaluate emerging AI technologies and provide recommendations regarding suitability, scalability, and business value. Act as the team's technology champion for next-generation AI capabilities. Stakeholder Engagement & Influence Partner with BI Leads, business stakeholders, and technology teams to identify opportunities for AI-driven transformation. Translate complex AI concepts, architectural decisions, and technical trade-offs into business-friendly language. Influence adoption of technical standards and solutions without direct reporting authority. Build credibility through expertise, collaboration, and delivery excellence. Act as a trusted advisor to stakeholders on AI opportunities, risks, limitations, and implementation strategies. Team Capability Development Mentor and coach data scientists on AI engineering practices, MLOps, software engineering principles, and Databricks development standards. Develop a Data Science Center of Excellence (CoE) focused on technical excellence and delivery quality. Drive knowledge-sharing sessions, workshops, and technical communities of practice. Create and maintain AI development playbooks, standards, checklists, and reusable assets. Key Deliverables Successful delivery of enterprise-grade Databricks Genie solutions. Establishment of AI engineering and MLOps standards across the team. Creation of reusable frameworks, templates, accelerators, and development assets. Improved solution scalability, maintainability, and deployment efficiency. Increased team capability in Agentic AI and Databricks development. Delivery of innovation initiatives and proof-of-concepts with measurable business impact. Continuous optimization of AI solution quality, latency, scala