Senior Data Scientist

MegaNucleus; Gurugram District; Hybrid; INR 1,800,000 - 2,400,000; Full time

Job summary

MegaNucleus in Gurgaon, India, is seeking a Senior Data Scientist to develop and deploy AI solutions, including LLM-based applications and multi-agent systems. You will design predictive models, implement RAG workflows, and ensure production readiness with governance and monitoring.

The role requires 6+ years in data science, strong Python, statistics, and hands-on experience with MLOps and cloud platforms. Hybrid work setup and cross-functional collaboration are expected.

Qualifications

• 6+ years in data science or applied ML.
• Production experience with regression/classification models.
• Experience deploying LLM-based or Generative AI applications.
• Hands-on with agentic frameworks like CrewAI, LangChain Agents, LangGraph.

Responsibilities

• Develop and deploy AI solutions across ML and genAI use cases.
• Design, build, validate and optimize predictive models including regression, trees and ensembles.
• Develop LLM-based and Generative AI solutions with RAG, prompt engineering and fine-tuning.
• Build multi-agent systems and tool-integrated AI solutions for analytics and conversational interfaces.
• Apply statistical analysis, hypothesis testing and experimental design.

Skills

Python

Statistics

ML techniques

LLMs & GenAI

Agentic frameworks

Vector databases

MLOps/CI-CD

Cross-functional comms

Education

Bachelor’s or Master’s in CS/DS

Quantitative field exposure

Job description

Our client is looking for a Senior Data Scientist . Our client is a market-leading, cloud-based CX benchmarking and operational improvement platform. This role is Hybrid, based out of Gurgaon (India).

Key Responsibilities

• Develop and deploy AI solutions across ML and other genAI use cases.
• Design, build, validate and optimize predictive models including regression, tree-based models, ensemble methods, gradient boosting and deep learning architectures where appropriate.
• Develop and deploy LLM-based and Generative AI solutions, including retrieval augmented generation (RAG), prompt engineering, fine-tuning and agentic workflows.
• Build multi-agent systems and tool-integrated AI solutions for analytics automation, insight generation and conversational interfaces.
• Apply statistical analysis, hypothesis testing and experimental design (including A/B testing) to inform business decisions.
• Collaborate with cross-functional teams
• Ensure production readiness through monitoring, model governance, performance tracking, retraining strategies and documentation.
• Analyze structured and unstructured datasets to generate actionable insights.
• Communicate analytical findings and technical concepts clearly to cross-functional stakeholders.
• Contribute to code reviews, best practices and continuous improvement of AI infrastructures.
• Independently manage projects in environments with evolving requirements and timelines.

Required Qualifications, Experience & Skills

Education:

• Bachelor’s or Master’s degree in Computer Science, Data Science,
• Statistics, Mathematics, Engineering, or a related quantitative field.

Experience:

• 6+ years of professional experience in data science or applied machine learning roles.
• Demonstrated experience delivering regression and/or classification models in production environments.
• Hands-on experience building and deploying LLM-based or Generative AI applications.
• Hands-on experience with agentic frameworks such as CrewAI, LangChain Agents, LangGraph or similar multi-agent orchestration frameworks.

Skills

• Strong proficiency in Python
• Solid foundation in statistics, regression, classification and other classical ML techniques.
• Hands-on experience with LLMs and Generative AI, including prompt engineering, RAG, fine-tuning and evaluation.
• Experience building multi-agent systems using frameworks such as CrewAI, LangChain/LangGraph or similar.
• Familiarity with vector databases and embedding pipelines.
• Experience deploying ML/LLM solutions in production with MLOps practices (e.g., MLflow, CI/CD) on cloud platforms (AWS/Azure/GCP/Databricks).
• Strong problem-solving and cross-functional communication skills.