Data Scientist Engineer Advisor

Data Scientist Engineer Advisor

Join NTT DATA UK's Data Practice at India offshore, 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 at India offshore. 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.

What you'll be doing:

• 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.
• 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.
• 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.

What experience you'll bring:

• 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.
• 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.

About NTT DATA: NTT DATA is a $30 billion business and technology services leader, serving 75% of the Fortune Global 100. We are committed to accelerating client success and positively impacting society through responsible innovation. We are one of the world's leading AI and digital infrastructure providers, with unmatched capabilities in enterprise-scale AI, cloud, security, connectivity, data centers and application services. Our consulting and industry solutions help organizations and society move confidently and sustainably into the digital future. As a Global Top Employer, we have experts in more than 50 countries. We also offer clients access to a robust ecosystem of innovation centers as well as established and start-up partners. NTT DATA is a part of NTT Group, which invests over $3 billion each year in R&D. Whenever possible, we hire locally to NTT DATA offices or client sites. This ensures we can provide timely and effective support tailored to each client's needs. While many positions offer remote or hybrid work options, these arrangements are subject to change based on client requirements. For employees near an NTT DATA office or client site, in-office attendance may be required for meetings or events, depending on business needs. At NTT DATA, we are committed to staying flexible and meeting the evolving needs of both our clients and employees.