NTT DATA UK&I is an ambitious business, and we want our people to be ambitious too: in your outlook, attitude and career goals. Building on our amazing heritage, we are embracing future opportunities with positivity, energy and commitment. Working together, we can create a positive, rewarding and inclusive culture.
Our commitment is to provide a forward-looking, equitable and rewarding place to work. We are developing programmes and initiatives to help our people to feel encouraged, appreciated and valued: we want you to feel like you belong here.
Your commitment is to reimagine and reinvent the possible, in your day-to-day work and how you interact with clients. This can mean challenging accepted wisdom, asking the right questions, making more meaningful connections. We’re working to provide an environment where that’s possible, in the belief that you cannot achieve your best without feeling energised and motivated.
The outcome is to create a place where you can thrive - where your growth and development are nurtured in a culture that celebrates creativity, innovation, and teamwork. We’re committed to fostering an environment that not only makes you want to stay but inspires you to reach new heights. At the heart of everything we do is our belief in the power of people, the deep, one-to-one relationships we build with you, and the lasting connections you create with clients. We want you to grow with us because when you succeed, we all shine.
Our mission: to accelerate client success and positively impact society through responsible innovation
Our values:
- Respect every voice. We grow by listening. We invite different viewpoints, honor every background and encourage sharing perspectives to learn from one another.
- Think big. Be bold. We stretch beyond what's expected. Curiosity fuels us, ambition drives us and innovation is how we push boundaries to shape the future.
- Deliver the outcome. We build trust by keeping our word. We act with integrity and hold ourselves accountable – always choosing to do the right thing.
- Win together. We lift each other up. We collaborate across borders, share openly and succeed as one global team.
Our inclusive work environment prioritises mutual respect, accountability, and continuous learning for all our people. We are also proud to share that we have a range of Inclusion Networks such as: the Women’s Business Network, Cultural and Ethnicity Network, LGBTQ+ & Allies Network, Neurodiversity Network and the Parent Network.
For more information on Diversity, Equity and Inclusion please click here: Creating Inclusion Together
Machine Learning & Advanced Analytics
- Develop machine learning models for forecasting, classification, recommendation, optimisation, clustering and anomaly detection.
- Apply statistical and machine learning techniques to analyse structured and semi-structured datasets.
- Perform exploratory data analysis, feature engineering, model training, validation and model evaluation.
- Compare model approaches, select appropriate metrics and explain technical trade-offs clearly.
- Build reusable notebooks, scripts and pipelines that support repeatable machine learning delivery.
- Support monitoring and continuous improvement of models used in pilot or production environments.
Client & Business Engagement
- Work with consultants and client stakeholders to understand business problems and translate them into analytical tasks.
- Frame ambiguous questions into testable hypotheses, analytical approaches and measurable outcomes.
- Communicate insights, model outputs and recommendations in a clear, practical and business-relevant way.
- Support proof-of-concepts, project delivery, estimates, solution options and technical documentation where required.
Platform Enablement
- Work with Data Engineering and Architecture teams to access, prepare and validate data for machine learning workloads.
- Use platforms such as Snowflake, Databricks and Microsoft Fabric where appropriate for data preparation, experimentation and deployment support.
- Follow good engineering practices for version control, testing, documentation and reproducibility.
- Contribute to MLOps ways of working, including model versioning, deployment handover and performance monitoring.
- Consider model explainability, data quality, bias, privacy and responsible AI implications where relevant.
Required:
- 3-5 years of commercial experience in Data Science, Machine Learning or Advanced Analytics roles.
- Strong hands-on Python experience for data analysis, feature engineering and model development.
- Strong SQL skills and experience working with complex or sizeable datasets.
- Solid theoretical and practical understanding of supervised learning, unsupervised learning, statistical modelling, feature engineering, validation, model evaluation and optimisation.
- Hands-on experience with Python-based machine learning libraries, especially Scikit-Learn and at least one of XGBoost, LightGBM, TensorFlow or PyTorch.
- Experience developing models beyond exploratory analysis into reusable, documented and testable assets, ideally with exposure to pilot or production environments.
- Strong understanding of model evaluation, including appropriate metrics, validation approaches, overfitting, data leakage, baseline comparison and business impact assessment.
- Ability to explain model assumptions, limitations, drivers and risks, including awareness of model explainability, bias, data quality and responsible AI considerations.
- Clear communication skills and the ability to work collaboratively in mixed technical and business teams.
Nice to have:
- Exposure to pilot or production deployment of machine learning models.
- Good understanding of MLOps concepts such as versioning, reproducibility, monitoring and deployment handover.
- Experience with at least one modern cloud data or analytics platform, such as Snowflake, Databricks, Microsoft Fabric, Azure, AWS or GCP.
- MSc or equivalent experience in Data Science, Statistics, Mathematics, Computer Science, Artificial Intelligence, Physics, Engineering or a related quantitative discipline.
- Experience with Spark or distributed data processing frameworks.
- Experience preparing data pipelines or analytical datasets to support machine learning workloads.
- Awareness of data engineering, data architecture, data governance and enterprise data platform concepts.
We offer a range of tailored benefits that support your physical, emotional, and financial wellbeing. Our Learning and Development team ensure that there are continuous growth and development opportunities for our people. We also offer the opportunity to have flexible work options.
For more information on NTT DATA UK & Ireland please click here: NTT DATA
We are an equal opportunities employer. We believe in the fair treatment of all our employees and commit to promoting equity and diversity in our employment practices. We are also a Disability Confident Committed Employer - we want to see every candidate performing at their best throughout the job application and interview process, if you require any reasonable adjustments during the recruitment process, please let us know and we look forward to hearing from you.
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 the growing Data Science capability within the Data Practice, you will work on Machine Learning, Predictive Analytics and Applied AI use cases that help clients move beyond traditional reporting towards evidence-led, AI-enabled decision making.
You will work alongside data engineers, data architects, platform specialists and consultants across Snowflake, Databricks and Microsoft Fabric to develop practical, production-ready machine learning solutions.
We are looking for a Data Scientist with a solid background in Machine Learning, Statistics and Predictive Analytics to join NTT DATA UK's Data Practice.
You will contribute hands-on across the model development lifecycle, from data understanding and feature engineering through to model training, evaluation, deployment support and ongoing improvement.
Experience with Generative / Agentic AI, LLMs and AI-enabled products is preferred, but the core of this role is strong applied machine learning capability rather than AI tooling alone.
