Deputy Manager-Data Science

Job Description

Group / Deputy Manager AI Engineering Conversational AI & Data Products (612 Years)

Overview

Function Overview

The AI Engineering function is focused on building next-generation conversational AI platforms that enable natural language interaction with enterprise data products for global pharmaceutical organizations. The team develops scalable AI systems that sit on top of commercial analytics data enabling business users to query explore and derive insights through LLM-powered conversational interfaces.

This function partners closely with Commercial Analytics Data Engineering Product and Business stakeholders to transform traditional dashboards into AI-native decision intelligence systems.

The role sits within the Conversational AI & Data Products team responsible for designing and deploying LLM-driven interfaces retrieval systems and AI agents that interact with governed enterprise data.

Roles and Responsibilities

• Design and develop conversational AI systems that enable natural language access to commercial analytics data products
• Build and deploy LLM-powered pipelines (RAG agents copilots) for querying structured and semi-structured enterprise data
• Translate business questions into AI workflows (e.g. KPI retrieval root-cause analysis anomaly explanation trend summarization)
• Develop semantic-aware retrieval layers integrating data warehouses (e.g. Snowflake Databricks) with LLMs
• Engineer prompt frameworks embeddings and context management pipelines to improve response accuracy and grounding
• Implement NL-to-SQL / NL-to-metrics translation systems aligned with governed semantic layers
• Collaborate with Data Engineering and BI teams to ensure AI readiness of data products (metadata lineage KPI logic ontologies)
• Build multi-turn conversational agents supporting follow-ups drill-downs and contextual reasoning
• Develop automated narrative generation systems for KPI insights and business storytelling
• Design and implement evaluation frameworks for LLM outputs (accuracy hallucination control explainability)
• Ensure solutions are scalable secure and compliant with enterprise governance and data privacy standards
• Optimize performance and cost of LLM systems (latency token usage caching strategies)
• Partner with product and business teams to identify and prioritize high-impact AI use cases
• Contribute to platform architecture reusable components and internal AI frameworks
• Mentor junior team members and contribute to AI engineering best practices

Core Competencies

Technical Skills

• Strong experience in LLM application development (e.g. GPT-based systems open-source LLMs)
• Hands-on with RAG (Retrieval-Augmented Generation) vector databases and embeddings
• Experience building NL-to-SQL / semantic query systems
• Proficiency in Python (preferred) and/or backend engineering (APIs microservices)
• Experience with LangChain LlamaIndex or similar orchestration frameworks
• Familiarity with Databricks Snowflake or modern data platforms
• Strong SQL skills and understanding of analytical data modeling
• Knowledge of semantic layers ontologies and KPI frameworks
• Experience with API integration and scalable AI system deployment (cloud: AWS/Azure/GCP)
• Understanding of LLM evaluation guardrails prompt engineering and hallucination mitigation

Domain & Functional Knowledge

• Familiarity with enterprise data products and BI ecosystems
• Ability to translate business questions into AI-driven analytical workflows
• Understanding of data governance lineage and compliance requirements

Behavioral & Professional Skills

• Strong problem-solving with a systems-thinking mindset
• Ability to work across AI data engineering and business teams
• Strong communication skills to explain AI system behavior and limitations
• Ownership mindset with ability to drive end-to-end AI solutions
• Adaptability to rapidly evolving LLM and AI ecosystems
• Focus on quality scalability and user-centric design

Must-Have Skills

• Experience building LLM-based applications (RAG copilots or agents)
• Strong Python SQL skills
• Hands-on experience with vector databases and embeddings
• Understanding of semantic data models and KPI-driven analytics
• Experience integrating AI with enterprise data platforms
• Strong communication and documentation skills

Good-to-Have Skills

• Experience with NL-to-SQL or conversational BI systems
• Exposure to multi-agent systems or autonomous AI workflows
• Familiarity with Databricks MLflow or AI notebooks
• Experience in pharma or healthcare analytics
• Knowledge of model fine-tuning LoRA or domain adaptation
• Exposure to AI explainability frameworks and governance

Education

• Bachelors or Masters degree in Computer Science AI Data Science Engineering or related field
• Strong foundation in machine learning NLP or distributed systems

Qualifications :

Preferred QualificationsExperience with cloud platforms such as AWS Azure or of MLOps CI/CD pipelines and containerization tools like Docker/ with databases vector databases and data engineering to LLMs NLP or Generative AI frameworks is a QualificationBE / BTech / MCA / MTech in Computer Science Engineering or a related field.

Remote Work :

No

Employment Type :

Full-time