At a Glance
- Tasks: Join a dynamic team to implement AI solutions on modern data platforms.
- Company: NTT DATA UK, a forward-thinking tech company focused on innovation.
- Benefits: Flexible work options, continuous learning, and tailored wellbeing support.
- Other info: Inclusive culture that values growth, creativity, and teamwork.
- Why this job: Make a real impact by applying AI to solve enterprise data challenges.
- Qualifications: 3-5 years in data roles with strong SQL and platform experience.
The predicted salary is between 49500 - 60500 £ per year.
The team you'll be working with: 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.
What you'll be doing:
- Applied AI on data platforms
- 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.
What experience you'll bring:
- Required:
- 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.
- Nice to have:
- Experience with Snowflake AI capabilities such as Cortex AI, Cortex Analyst, Cortex Search, Cortex Agents, Snowpark or Snowflake-native governance features.
- Experience with Databricks AI capabilities such as Mosaic AI, Genie, AI/BI, Vector Search, MLflow, Unity Catalog, Model Serving or agent-related features.
- Experience with Microsoft Fabric capabilities such as Copilot in Fabric, Data Factory, Data Engineering, Data Science, Power BI semantic models, OneLake, Direct Lake, Fabric Data Agents or Fabric governance capabilities.
- Experience implementing AI-enabled analytics, conversational data products, semantic search or enterprise knowledge retrieval solutions.
- MSc or equivalent professional experience in Data, Computer Science, Information Systems, Engineering, Mathematics, Analytics, Business Intelligence or a related discipline.
- Experience with metadata management, semantic modelling, data catalogues, business glossaries, metric layers or BI semantic layers.
- Exposure to enterprise architecture, solution design, platform governance or cloud data platform implementation.
- Experience with DevOps or DataOps practices, including version control, CI/CD, deployment pipelines and environment management.
- Experience working in consulting, client delivery or internal platform enablement roles.
Data Scientist – Applied AI & Data Platforms in London employer: NTT DATA
At NTT DATA, we pride ourselves on being an excellent employer that fosters a culture of innovation and collaboration. Our commitment to employee growth is evident through continuous learning opportunities and a supportive environment that encourages creativity in the rapidly evolving field of AI/ML security. Located in a vibrant tech hub, we offer competitive benefits and a dynamic workplace where your contributions directly impact the future of secure AI systems.
StudySmarter Expert Advice🤫
We think this is how you could land Data Scientist – Applied AI & Data Platforms in London
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We think you need these skills to ace Data Scientist – Applied AI & Data Platforms in London
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!
Craft a Tailored Cover Letter:For a full-time role at NTT DATA, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at NTT DATA. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at NTT DATA
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
✨Showcase Your Projects
Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at NTT DATA!
✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
