Join NTT DATA's Data Practice in India as a Data Scientist Senior Advisor. This role is crucial for driving enterprise-scale data platforms and analytics, transforming clients' decision-making through AI and Machine Learning. You'll be part of a dynamic, multi-disciplinary team focused on advanced analytics and applied AI, working with cutting-edge technologies to deliver production-ready solutions.

This position offers the opportunity to contribute significantly to our growing Data Science capability, helping clients evolve from traditional reporting to AI-enabled insights. You will collaborate closely with data engineers, architects, and consultants on platforms like Snowflake, Databricks, and Microsoft Fabric.

Develop advanced machine learning models for forecasting, classification, recommendations, optimization, clustering, and anomaly detection.

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

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

Collaborate with consultants and clients to understand business challenges and translate them into actionable analytical tasks.

Communicate complex insights, model outcomes, and recommendations in a practical, business-relevant manner.

Work with Data Engineering and Architecture teams to access and prepare data for ML workloads, leveraging platforms like Snowflake, Databricks, and Microsoft Fabric.

Contribute to MLOps practices, including model versioning, deployment, and performance monitoring, while considering explainability and responsible AI principles.

A minimum of 6-10 years of commercial experience in Data Science, Machine Learning, or Advanced Analytics is required.

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

A solid theoretical and practical understanding of supervised and unsupervised learning, statistical modeling, and model evaluation techniques.

Hands-on experience with Python ML libraries such as Scikit-Learn, XGBoost, LightGBM, TensorFlow, or PyTorch.

Proven ability to develop models into documented, testable, and reusable assets, ideally with exposure to production environments.

Strong communication skills and the ability to collaborate effectively within mixed technical and business teams are essential.