Head of Data Platform and Machine Learning

About the Company Our client is one of India’s fastest-growing technology-led Non-Bank Financial Companies (NBFCs), established in 2019. The organization operates across wholesale lending, direct lending, and tech-enabled partnerships with NBFCs and fintechs. Its technology-first underwriting and decisioning model enables lending at scale, addressing India’s significant credit gap—particularly for underserved and underpenetrated segments. The company is regulated by the Reserve Bank of India (RBI) and holds top-tier credit ratings from leading agencies. It operates a nationwide physical branch network, manages over a million active loans, oversees a multi-billion-dollar AUM, and employs ~1,000 professionals in India. The business is part of a global financial services group headquartered in Asia, with operations spanning India, Southeast Asia, and the Americas. The parent group has a multi-decade legacy as a diversified financial services leader across lending, payments, leasing, real estate, and related domains. Role Overview This role will lead Data Engineering and Machine Learning Engineering across the organization, owning the design, build, and operation of enterprise-scale data and decisioning platforms . The position partners closely with Product, Risk, Credit, and Business leaders to enable scalable, compliant, and explainable decision-making across the full credit lifecycle. Key Responsibilities Data & ML Engineering Leadership Build, scale, and lead data engineering and ML engineering functions. Establish robust MLOps frameworks for standardized, production-grade model development, deployment, and monitoring. Ensure smooth transition of models from experimentation to live production environments. Enterprise Decisioning Platform Design and operationalize a centralized decisioning platform integrating low-code model development, AutoML, rule engines, and workflow orchestration. Enable Data Science and Risk teams to build, test, and deploy models with minimal engineering dependency. Scale decisioning capabilities across credit, pricing, collections, fraud, cross-sell, and customer management. Ensure platforms are modular, scalable, explainable, auditable, and compliant with regulatory requirements. Core Platform & Lifecycle Management Architect and operate modern data platforms including real-time ingestion, lakehouse architectures, and event-driven systems. Own end-to-end data lifecycle management from sourcing to archival. Partner with governance teams to ensure lineage, auditability, and regulatory compliance. Operational Excellence Lead DataOps, SRE, and L1/L2 support teams to deliver >99.5% platform uptime. Implement automated testing, proactive monitoring, and self-healing systems. Drive infrastructure efficiency and cloud cost optimization at scale. Business Delivery & Stakeholder Engagement Act as a strategic execution partner to Product, Strategy, Risk, and Business leadership. Deliver platforms and decisioning capabilities aligned to core business KPIs such as loan growth, risk reduction, ticket-size expansion, and collections efficiency. Manage technology partnerships and vendor ecosystems (e.g., cloud, data platforms, automation tools). Required Skills & Qualifications 15–20 years of experience in data engineering, ML engineering, or platform leadership, including 8–10 years in senior leadership roles. Proven track record of building and scaling large-scale data and ML platforms in fast-paced environments (fintech or regulated industries preferred). Strong academic background with a Bachelor’s/Master’s/PhD in Computer Science, Engineering, or quantitative disciplines from top-tier institutions. Deep expertise in data platform architecture including streaming, lakehouse, event-driven systems, and real-time ingestion. Hands-on experience with MLOps frameworks and tooling (e.g., MLflow, Kubeflow, Airflow, SageMaker, Vertex AI) . Strong understanding of model lifecycle management including deployment, monitoring, retraining, and governance. Experience operationalizing enterprise decisioning platforms combining rules, ML, AutoML, and workflow automation. Expertise in distributed systems and big data technologies (Spark, Hadoop, Kafka, Flink). Proficiency with major cloud platforms (AWS, GCP, Azure) and container orchestration (Kubernetes, Docker). Solid grounding in data governance, lineage, and compliance within regulated environments. Strong programming skills in Python, SQL, Scala and/or Java, with working knowledge of ML/DL frameworks (TensorFlow, PyTorch, Scikit-learn). Demonstrated success driving reliability, resilience, and performance through DataOps and SRE practices. Experience managing and optimizing cloud infrastructure costs at scale. Proven people leadership experience managing teams of 15+ engineers. Excellent communication, stakeholder management, and vendor negotiation skills, with the ability to bridge business and technology.