Join NTT DATA as a Data Scientist Senior Advisor, contributing to cutting-edge data platforms and analytics. This role is pivotal in driving enterprise-scale data transformations and AI-enabled decision-making for clients.

As part of a dynamic Data Practice, you will focus on Machine Learning, Predictive Analytics, and Applied AI use cases. You'll collaborate with data engineers, architects, and consultants on platforms like Snowflake, Databricks, and Microsoft Fabric to build production-ready ML solutions.

This position is ideal for a seasoned Data Scientist with a strong foundation in Machine Learning and Statistics, eager to advance beyond traditional reporting into AI-driven insights. Experience with Generative AI, LLMs, and AI products is a plus, but core applied ML expertise is paramount.

Develop and implement advanced machine learning models for diverse applications including forecasting, classification, recommendation, optimization, clustering, and anomaly detection. Analyze structured and semi-structured data using statistical and ML techniques, performing comprehensive exploratory data analysis, feature engineering, and model validation.

Translate complex business problems into analytical tasks, framing ambiguous questions into testable hypotheses and measurable outcomes. Communicate findings, model outputs, and actionable recommendations clearly to both technical and business stakeholders. Support proof-of-concepts, project delivery, and technical documentation.

Collaborate with Data Engineering and Architecture teams to ensure data availability and quality for ML workloads. Utilize platforms like Snowflake, Databricks, and Microsoft Fabric for data preparation, experimentation, and deployment. Adhere to best practices in version control, testing, documentation, and reproducibility, contributing to MLOps principles and ensuring responsible AI considerations.

A minimum of 6-10 years of professional experience in Data Science, Machine Learning, or Advanced Analytics roles is required. Proficiency in Python for data analysis, feature engineering, and model development, alongside strong SQL skills for working with complex datasets, is essential.

A solid grasp of supervised and unsupervised learning, statistical modeling, feature engineering, validation, and model optimization is expected. Hands-on experience with Python ML libraries like Scikit-Learn, XGBoost, LightGBM, TensorFlow, or PyTorch is crucial.

Demonstrated ability to develop models beyond exploration into reusable, documented, and testable assets, preferably with production exposure. Strong communication and collaboration skills are necessary for working effectively within mixed technical and business teams. Understanding of model evaluation, including metrics, validation, overfitting, and business impact, is vital.