Employment Type: Full-time, hybrid work arrangement
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
812+ 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 deliverypragmatic and risk-based.
Leadership, mentoring, and cross-functional influence.
Job Classification
Industry: Software ProductFunctional Area / Department: Data Science & AnalyticsRole Category: Data Science & Analytics - OtherRole: Data Science & Analytics - OtherEmployement Type: Full time