Head of DS ML and AI Platform Engineering

Responsibilities:

• Data Science and AI Leadership: Build, scale, and mentor high-performing data science, machine learning, and AI teams, and support data engineering teams, establishing best practices across model development, advanced analytics, and AI delivery.
• Enterprise Decisioning Platform: Design and scale a centralized decisioning platform integrating predictive models, low-code model development, AutoML, real-time rule engines, policy-as-code, and automated workflows.
• Advanced Analytics and Predictive Modelling: Drive the development and implementation of advanced statistical models, predictive analytics, machine learning, and deep learning solutions to support enterprise-wide business decisions.
• MLOps and LLMOps Standardization: Establish production-grade MLOps frameworks and LLM/Agentic AI infrastructure to streamline model development, evaluation, deployment, and lifecycle management.
• Production Deployment and Scaling: Partner closely with Data Science and ML Engineering teams to transition machine learning and deep learning models from experimentation into scalable, high-availability production environments.
• AI and ML Platform Architecture: Drive the strategic vision for scalable AI/ML platforms, leveraging modern cloud-native data ecosystems, including Databricks, Delta Lake, Unity Catalog, DLT, and Apache Spark.
• Model Governance and Regulatory Compliance: Establish comprehensive model lifecycle governance and regulatory compliance frameworks aligned with RBI FREE-AI guidelines, DPDP Act requirements, and internal risk policies.
• Model Performance and Observability: Implement monitoring frameworks across model accuracy, predictive performance, model drift, feature freshness, retraining triggers, and platform health.
• FinOps and Cloud Optimization: Drive cost optimization across cloud and AI/ML infrastructure footprints, including AWS, Databricks, and third-party platforms.
• Data Governance, Security, and Lineage: Ensure robust data governance, lineage, access controls, PII masking, data cataloging, and audit-ready documentation to support reliable and compliant AI/ML operations.
• Decision Science and Business Applications: Expand AI-driven decisioning capabilities across credit underwriting, risk assessment, loan pricing, collections, fraud detection, and customer management.
• Cross-Functional and Executive Alignment: Partner with the Head of Product and Strategy, Chief Risk Officer (CRO), and functional leadership to translate business challenges into high-impact data science and AI initiatives.
• Operational Excellence and Reliability: Oversee model deployment reliability, automated testing, CI/CD pipelines, incident management, root-cause analysis, and continuous improvement across AI/ML platforms.
• Culture and AI-Native Innovation: Build and retain top data science and AI talent while championing AI-native development workflows through coding agents, LLMs, and automated tooling.

Requirements:

• Overall Experience: 15-20 years of experience across data science, advanced analytics, machine learning, AI, and related technology domains, with at least 8-10 years in senior management or leadership roles.
• Top-Tier Education: Bachelor's, Master's, or PhD in Computer Science, Mathematics, Statistics, Data Science, Software Engineering, or a related quantitative field from a premier institution (IIT, IISc, BITS, NIT, or an international equivalent).
• Data Science and Advanced Analytics Expertise: Strong expertise in statistical modelling, predictive analytics, machine learning, deep learning, forecasting, optimization, feature engineering, and advanced analytical techniques.
• Modern Data and AI Ecosystem: Deep understanding of modern data and AI platforms, including Databricks (Delta Lake, Unity Catalog, DLT, MLflow, Mosaic AI), Apache Spark, Kafka, and Lakehouse architectures.
• MLOps and AI Infrastructure: Proven track record of building and operating enterprise MLOps platforms, feature stores, model registries, evaluation frameworks, and LLM/Agentic AI infrastructure (RAG, LangGraph, CrewAI).
• Decisioning Platform Expertise: Experience operationalizing centralized real-time decisioning platforms integrating predictive models, business rule engines, policy-as-code, and real-time scoring APIs.
• Cloud and Infrastructure Architecture: Advanced proficiency in cloud-native platforms (AWS/GCP), container orchestration (Kubernetes, Docker), event-driven microservices, and Infrastructure-as-Code (Terraform, CloudFormation).
• Programming and Technical Expertise: Strong proficiency in Python, SQL, and relevant data science/ML frameworks, including Pandas, NumPy, Scikit-learn, TensorFlow, and PyTorch. Exposure to Scala/Java, automated testing, and CI/CD pipelines.
• Regulatory and Compliance Fluency: Experience operating within regulated financial environments, with exposure to RBI digital lending guidelines, the RBI FREE-AI framework, the DPDP Act, and GDPR compliance.
• Model Observability and Performance Management: Strong understanding of model monitoring, model drift, predictive performance, model explainability, and observability frameworks using tools such as Datadog, Grafana, OpenTelemetry, and Databricks Lakehouse Monitoring.
• FinOps and Cost Management: Experience managing large-scale AI/ML infrastructure budgets, optimizing cloud compute utilization, and driving cost efficiency across enterprise platforms.
• Organizational Leadership: Proven track record of building, mentoring, and managing multidisciplinary data science, AI/ML, and technical organizations of 20+ professionals.
• Fintech/Lending Domain Expertise: Substantial experience within fintech, NBFC, or retail banking environments, particularly across credit risk, underwriting, collections, fraud detection, and decision science.
• Delivery and Program Management: Expertise in Agile/Scrum execution, OKR/KPI design, program management, dependency management, technical documentation, and cross-functional execution.
• Executive and Vendor Stakeholder Management: Exceptional communication and stakeholder management skills, with the ability to translate complex technical and analytical concepts into business outcomes, align cross-functional leadership, and manage enterprise technology partnerships.