NTT DATA is seeking an innovative and passionate Data Scientist Engineer Advisor to join our team in Bangalore. You will be part of NTT DATA UK's Data Practice at India offshore, a dynamic multi-disciplinary team focused on delivering enterprise-scale data platforms and driving data-led transformations for clients. This role offers the opportunity to work on cutting-edge Machine Learning, Predictive Analytics, and Applied AI use cases, empowering clients to make evidence-led, AI-enabled decisions.

You will collaborate closely with data engineers, architects, and consultants across platforms like Snowflake, Databricks, and Microsoft Fabric to build practical, production-ready machine learning solutions. If you are looking to grow your career in a forward-thinking and inclusive organization, this is an excellent opportunity.

Develop and refine machine learning models for various applications, including forecasting, classification, recommendations, optimization, clustering, and anomaly detection.

Apply statistical and machine learning techniques to analyze diverse datasets, performing exploratory analysis, feature engineering, model training, and validation.

Clearly articulate model trade-offs, select appropriate evaluation metrics, and build robust, reusable code and pipelines for repeatable machine learning delivery.

Collaborate with consultants and clients to understand business challenges, translate them into analytical tasks, and frame ambiguous questions into testable hypotheses and measurable outcomes.

Communicate complex insights, model outputs, and recommendations in a clear, practical, and business-relevant manner, supporting proof-of-concepts and technical documentation.

Partner with Data Engineering and Architecture teams to access and prepare data for machine learning workloads, utilizing platforms like Snowflake, Databricks, and Microsoft Fabric.

Adhere to strong engineering practices for version control, testing, and reproducibility, contributing to MLOps workflows including model versioning and performance monitoring.

Consider and address critical aspects like model explainability, data quality, bias, privacy, and responsible AI.

A minimum of 3-5 years of commercial experience in Data Science, Machine Learning, or Advanced Analytics roles is essential.

Proficiency in Python for data analysis, feature engineering, and model development, along with strong SQL skills for working with complex datasets.

Possess a solid theoretical and practical understanding of supervised and unsupervised learning, statistical modeling, feature engineering, validation, and model optimization.

Demonstrated hands-on experience with Python ML libraries such as Scikit-Learn and at least one of XGBoost, LightGBM, TensorFlow, or PyTorch.

Experience developing models beyond initial exploration into reusable, documented, and testable assets, ideally with exposure to pilot or production environments.

Strong grasp of model evaluation techniques, including appropriate metrics, validation strategies, and business impact assessment.

Ability to clearly articulate model assumptions, limitations, drivers, and risks, with an understanding of explainability, bias, and responsible AI considerations.

Excellent communication skills and the ability to thrive in collaborative environments with both technical and business teams.