The job :

We are seeking a Lead Data Scientist to drive end-to-end model development initiatives. This role will be responsible for translating business problems into scalable machine learning solutions, leading model design and validation, and ensuring measurable business impact through data-driven insights. The ideal candidate combines strong statistical and machine learning expertise with hands-on Python experience and leadership capability. Candidate will analyze the customer data for correctness, quality checks, building context and co-relation. He will assist and build reusable ML use case templates for cloud practice under Global Services, analyse customer data to use for the templatized use cases.

Key Responsibilities :

- Lead data exploration, data quality assessment, data patterns and identification of data gaps; coordinate with data engineering to enable reliable datasets.

- Drive the complete ML lifecycle from data exploration and feature engineering to model development and validation.

- Design and develop feature engineering pipelines and select appropriate ML approaches (baseline - advanced).

- Analyze the customer data for correctness, quality checks, building context and co-relation.

- Asist in building, training, optimizing, and testing models using AVEVA products, Python or another analytics tool.

- Part of Cloud practice under global services, to play key role of data scientist, who will work with customer and internal team.

- Develop classification, regression, clustering, and forecasting models using appropriate algorithms and tuning techniques.

- Select appropriate algorithms based on problem context.

- Establish robust evaluation: validation strategy, metrics, error analysis, bias checks, and model explainability.

- Identify new AI opportunities, contribute to roadmap planning, and promote data-driven decision-making.

Desired Skills :

- 8 to 12+ years in Data Science / Machine Learning.

- Strong expertise in Python.

- Experience with deep learning frameworks preferred.

- Strong foundation in statistics, probability, and mathematics.

- Strong understanding of analyzing customer data.

- Exposure to cloud platforms (AWS, Azure, GCP) is desirable.

- Ability to interpret complex data and communicate insights clearly.

- Experience with supervised learning (classification/regression) and unsupervised learning (clustering).

- Time series/NLP/deep learning (as relevant to role).

- Experience with tools like Data Bricks or similar is desirable.

- Experience in industry like Oil & Gas, Utilities, Water, Data Center or CPG.

- Experience in developing forecasting, prediction, prescription or anomaly detection models for above industries.

- Strong communication: translate security risk into engineering actions and business impact.

- Ability to drive adoption without blocking delivery pragmatic and risk-based.

- Leadership, mentoring, and cross-functional influence.