Data Science Manager / Sr Manager

Job Description :

At least 8 + years of experience in AI / ML, including :

A minimum of 2 years leading generative AI applications , focusing on large language models (LLMs), diffusion models, or other advanced AI technologies.

3+ years of experience in NLP-based applications such as ChatBots, Text Classification, Named Entity Recognition (NER), or other NLP-driven projects.

Experience with frameworks and tools for building agentic pipelines, such as LangGraph , LLamaIndex , Autogen , PromptFlow , and dspy .

Expertise in advanced prompt engineering techniques, including ReACT , Chain of Thought , and Tree of Thought prompting methodologies.

Proficiency in evaluating LLM-based applications using frameworks like RAGAS for retrieval-augmented generation and application scoring.

Should have strong knowledge on LLM’s foundational model (OpenAI GPT4o, O1, Claude, Gemini etc), while need to have strong knowledge on opensource Model’s like Llama 3.2, Phi etc.

Roles & Responsibilities :

Lead a team of Data Engineers, Analysts and Data scientists to carry out following activities :

Connect with internal / external POC to understand the business requirements

Coordinate with right POC to gather all relevant data artifacts, anecdotes, and hypothesis

Create project plan and sprints for milestones / deliverables

Spin VM, create and optimize clusters for Data Science workflows

Create data pipelines to ingest data effectively

Assure the quality of data with proactive checks and resolve the gaps

Carry out EDA, Feature Engineering & Define performance metrics prior to run relevant ML / DL algorithms

Research whether similar solutions have been already developed before building ML models

Create optimized data models to query relevant data efficiently

Run relevant ML / DL algorithms for business goal seek

Optimize and validate these ML / DL models to scale

Create light applications, simulators, and scenario builders to help business consume the end outputs

Create test cases and test the codes pre-production for possible bugs and resolve these bugs proactively

Integrate and operationalize the models in client ecosystem

Document project artifacts and log failures and exceptions.

Measure, articulate impact of DS projects on business metrics and finetune the workflow based on feedback