The Assistant Manager - Data Science is responsible for developing, deploying and

operationalizing data science and machine learning solutions that support AI, analytics and

automation use cases. The role works with Solution Architects, Data Engineers and

business teams to translate business problems into analytical models and insights,

leveraging the data architecture defined by the Architecture Lead and Solution Architect.

The position ensures robust, accurate and scalable models for use cases such as

Manufacturing Control Tower, Golden Batch, Yield Improvement, OOS prediction and

pricing models.

1. Roles Played

• Assistant Manager - Data Science for AI and analytics initiatives.


• Owner of data science models, analytical workflows and model performance SLAs.


• Key contributor to feature engineering, model quality and responsible AI / governance practices.

2. Key Platforms / Technologies


• Azure Machine Learning, Databricks, Python, LLM/GenAI frameworks and ML model development platforms.


• Statistical analysis, machine learning, deep learning and LLM orchestration pipelines.


• Batch and real-time analytics / scoring frameworks.


• BI environments (Power BI, Tableau) for insight consumption and model output visualization.


• Integration with SAP ECC or S/4 HANA, MES, LIMS, Empower, CTMS and other enterprise sources for model development, LLM use cases and analytics.

4. Overall Job Responsibilities

A. Data Science Solution Design & Model Development


• Design data science, machine learning and LLM solutions as per architecture guidelines.


• Build analytical models, feature sets and decision-support solutions to support AI, analytics and reporting use cases.


• Ensure solutions are modular, reusable, interpretable and performance optimized.

B. Data Science Delivery & Operations (≈ 25%)


• Lead the development, deployment and operationalization of data science, machine learning and LLM solutions for business use cases.


• Set up and maintain model pipelines, monitoring, alerting, logging and retraining workflows for analytical solutions.


• Own day-to-day model operations and incident resolution related to model performance, drift and reliability.

C. Model Quality, Governance & Security (≈ 20%)


• Implement model validation rules, performance checks and output quality monitoring.


• Ensure feature lineage, experiment tracking and documentation for key models and datasets.


• Work with Security and Governance teams to enforce access control, privacy, responsible AI and data integrity requirements.

D. Performance, Optimization & Cost Management (≈ 10%)


• Optimize models, inference pipelines and compute utilization for performance and cost efficiency.


• Periodically review resource utilization and recommend improvements in model design, training and deployment.

E. Collaboration & Stakeholder Engagement (≈ 10%)


• Collaborate with Solution Architect, Data Engineers, Full Stack Developers and Business Analysts.


• Understand business problems and analytical needs of each use case and translate them into data science tasks and solutions.

F. Team Leadership & Capability Building (≈ 10%)


• Lead and mentor Data Scientists and ML developers.


• Promote best practices in model development, testing, documentation, MLOps, LLMOps and responsible AI.

5. External Interfaces

• Data platform vendors and implementation partners.

6. Internal Interfaces

• Solution Architect and Architecture Lead.
• BI Developers, Data Engineers and Modelers.
• Application team (SAP, MES, LIMS etc.), Infrastructure and Security.

8. Education

• Bachelor’s degree in engineering / computer science / IT – mandatory.
• Postgraduate qualification in Data Engineering / Data Science – preferred.

9. Experience


• 4–8 years of experience in data science, machine learning, advanced analytics or AI roles.


• Minimum 3–5 years leading data science teams or complex analytics / AI projects.


• Hands-on experience with cloud-based data science and ML platforms (preferably Azure).


• Pharma or manufacturing analytics experience is an advantage.

10. Knowledge & Skills (Functional / Technical)


• Strong skills in Python, SQL/NoSQL, Databricks, Spark and machine learning / statistical modeling frameworks.


• Experience with Azure Machine Learning, Azure data platforms, Microsoft Fabric, Delta Lake and cloud-based analytics architecture.


• Familiarity with LLM/GenAI frameworks, MLOps / LLMOps and scripting for model development and deployment.


• Understanding of model quality, governance and metadata / feature management concepts.


• Exposure to integration with SAP, MES, LIMS or similar systems for advanced analytics and AI use cases.

11. Leadership / Managerial Attributes

• Hands-on technical leader with a delivery focus.
• Good communication and coordination with multiple teams.
• Ability to mentor junior engineers and enforce engineering discipline.

12. Other Requirements

• Relevant cloud/data certifications (e.g., Azure Data Scientist) preferred.
• Willingness to support critical data operations outside normal hours when required.