Data Scientist, NTT DATA

• 3-5 years of experience in a data-focused role, such as Data Engineer, Analytics Engineer, Data Analyst, BI Developer, Data Platform Consultant or Applied AI Consultant
• Hands-on experience with at least one modern data platform, such as Snowflake, Databricks, Microsoft Fabric, Azure, AWS or GCP, with willingness to develop capability across multiple platform ecosystems
• Strong SQL skills and practical experience working with structured and semi-structured data
• Good understanding of data engineering or analytics engineering concepts, including data modelling, pipelines, ELT/ETL, data quality and analytical data products
• Experience using Python, PySpark, notebooks or equivalent platform tooling for data preparation, analysis, testing or automation
• Understanding of semantic models, business definitions, metrics, metadata and how they improve the reliability of AI-enabled analytics
• Awareness of Generative AI concepts, including prompts, embeddings, vector search, semantic search, RAG, AI agents and natural language analytics
• Ability to configure, test and validate platform AI capabilities using real data, metadata, semantic models and business scenarios, rather than only discussing AI concepts at a high level
• Understanding of enterprise delivery considerations such as security, access control, privacy, governance, documentation, cost management and operational handover
• Clear communication skills and ability to work in mixed teams across data engineering, architecture, analytics and business stakeholders

Desirable

• You will join NTT DATA UK's Data Practice, a multi-disciplinary team delivering enterprise-scale data platforms, analytics and data-led transformation for clients
• As part of our growing Data Science capability, this role helps clients adopt Applied AI capabilities embedded in modern data platforms. The focus is to make those capabilities practical, secure, governed, scalable and enterprise-ready
• You will work alongside data engineers, data architects, analytics specialists, platform consultants and client stakeholders to design and deliver AI-enabled data solutions that sit close to enterprise data
• We are looking for a Data Scientist - Applied AI & Data Platforms to help clients implement AI-enabled data solutions on modern data platforms
• This is a platform-aligned Applied AI role within the Data Science capability. It is not focused on deep Machine Learning research or building custom models from scratch; it is focused on applying platform-native AI capabilities to enterprise data problems in a governed and scalable way
• The role is suitable for someone with a strong grounding in data platforms, data engineering, analytics engineering, BI or data analytics, and who is keen to build deeper capability in Applied AI
• You will help design, configure, test and implement capabilities such as Snowflake Cortex AI, Databricks AI/BI and Mosaic AI capabilities, Microsoft Fabric Copilot, Fabric Data Agents, semantic models, vector search, RAG patterns, AI-assisted data classification and natural language analytics
• The role requires someone practical and delivery-focused: able to move from proof of concept to controlled enterprise implementation, with appropriate attention to governance, security, data quality, performance, monitoring, cost and supportability
• Configure, test and implement AI-enabled capabilities within modern data platforms, with depth in at least one of Snowflake, Databricks or Microsoft Fabric
• Support use cases such as natural language querying, AI-assisted analytics, document and data search, data classification, automated insight generation and conversational access to enterprise data
• Build and configure platform-native AI features such as Cortex Analyst, Cortex Search, Cortex Agents, Databricks AI/BI, Genie, Mosaic AI capabilities, Microsoft Fabric Copilot and Fabric Data Agent-style solutions where appropriate
• Ground AI outputs in governed data sources, semantic models, metadata, business definitions and reusable data products
• Support implementation of Retrieval Augmented Generation (RAG), vector search, semantic search and enterprise knowledge retrieval where aligned to client needs
• Evaluate AI-generated outputs for accuracy, grounding, relevance, hallucination risk, data leakage and business usability. Data engineering, analytics and platform delivery
• Prepare and structure data so that it can be used effectively by AI-enabled analytics and data platform capabilities
• Work with SQL, Python, PySpark, notebooks or platform-native tooling to transform, test and expose data for downstream AI and analytics use cases
• Collaborate with data engineers and architects to ensure AI solutions are built on reliable, secure and well-governed data foundations
• Support implementation of pipelines, notebooks, semantic layers, governed datasets and analytical data products
• Help define reusable implementation patterns for AI-enabled data products across Snowflake, Databricks and Microsoft Fabric
• Contribute to documentation, technical designs, test plans, operational handover materials and reusable delivery playbooks
• Enterprise readiness and governance:
• Help make AI-enabled solutions enterprise-ready by considering access control, security, privacy, data lineage, observability, cost management and operational support
• Work with platform governance features such as role-based access control, catalogues, sensitivity labels, lineage, model or agent governance and auditability
• Support responsible AI practices, including transparency, explainability, human oversight and appropriate control of AI-generated outputs
• Test AI-enabled outputs for quality, reliability, business relevance, potential misuse and maintainability
• Ensure solutions are designed for controlled adoption and supportability, not just demo value
• Client and stakeholder engagement:
• Work with client stakeholders to understand business problems and identify where platform-native AI capabilities can help
• Translate business requirements into practical AI-enabled data platform solutions, delivery patterns and measurable outcomes
• Explain technical concepts clearly to both technical and non-technical audiences
• Support demos, proof-of-concepts, workshops, internal enablement sessions and client adoption activities
• Contribute to reusable assets, accelerators, templates and capability-building material for the Data Practice