Lead - Data Scientist

Data Architecture & Data Lake/Lakehouse Leadership:

- Build, lead and scale the Data Science team, defining organisation structure, roles, hiring priorities, capability roadmap and succession plans.

- Own the Data Science strategy and roadmap within the AI Track, aligned with enterprise AI priorities and business objectives.

- Lead identification, prioritisation and shaping of high-value Data Science, AI/ML, GenAI and advanced analytics use cases.

- Provide technical direction to Data Scientists; review modelling approaches, experimentation, evaluation and solution quality.

- Act as the Data Science and Data Architecture lead for the AI Track and take end-to-end ownership of designing, architecting and establishing the enterprise Data Lake / Lakehouse from scratch, while driving and coordinating the required engineering implementation.

- Assess existing enterprise data sources, systems, databases and data flows and define the target-state data architecture required for AI and advanced analytics.

- Define the Data Lake / Lakehouse architecture, including data ingestion, landing/raw, curated and consumption layers, storage, processing, orchestration, metadata and data-serving patterns.

- Define data models, domain structures, data contracts and integration patterns required to make enterprise data usable for Data Science and AI.

- Establish data ingestion and pipeline strategy for batch, near-real-time and streaming data, working with relevant engineering teams to implement the architecture.

- Define data quality, lineage, cataloguing, metadata, master/reference data, retention and lifecycle management practices.

- Define security, access control, privacy and governance requirements for the AI data platform in partnership with Information Security and enterprise architecture teams.

- Establish scalable data architecture that supports Data Science experimentation as well as production model training, inference, analytics and GenAI use cases.

- Evaluate technology choices and make architecture recommendations across data storage, processing, orchestration, analytics and AI/ML platforms, considering scalability, performance, resilience and cost.

- Establish standards and best practices for the Data Science lifecycle, model development, evaluation, documentation, governance and production readiness.

- Ensure effective conversion of Data Science ideas and POCs into scalable, production-grade solutions with measurable business outcomes.

- Build strong collaboration between Data Science, Data Engineering, AI Engineering, Architecture, Product, Infrastructure and business teams.

- Establish reusable frameworks, accelerators and reference architectures for AI and Data Science delivery.

- Promote responsible AI, model risk management, data quality, security and governance across the Data and AI lifecycle.

Skills

Data Science, Analytics, Data Analytics, Machine Learning, Data Modeling, Data Management, Data Scientist, Artificial Intelligence, Data Quality, IT Jobs in BFSI