Lead Data Scientist

Fractal Analytics; Pune District, Bengaluru, New Delhi; On-site; INR 4,000,000 - 7,000,000; Full time

Job summary

Fractal Analytics is seeking an accomplished Lead Data Scientist with 10+ years of experience, deeply skilled in classical machine learning, GenAI applications, and ML lifecycle management. The role involves architecting GenAI-based solutions, building LLM systems, vector-search pipelines, and deploying robust ML pipelines across cloud platforms, with leadership responsibilities.

You will lead the technical direction for AI features, collaborate with product teams, and guide engineers in

Qualifications

• 10+ years in Classical ML, GenAI & ML-Ops.
• Strong experience in Python, PySpark, SQL, Scikit-Learn, XGBoost, LightGBM, Random Forest.
• LangChain, LangGraph, LangSmith (tracing, metrics, evaluations).
• MLflow/Sagemaker/Databricks containerization and model deployment.
• Docker, Git-Ops; production-grade GenAI applications.

Responsibilities

• Machine learning and statistical modelling across projects.
• Lead GenAI and LLM system architectures; build RAG pipelines and vector DB integrations.
• Develop agentic systems and MCP; design toolcalling workflows and multi-agent coordination.
• Cloud ML-Ops with emphasis on data drift, model quality, and reproducibility.
• Provide technical leadership and collaborate with product teams to define AI features.

Skills

Classical ML

GenAI

ML-Ops

Python

PySpark

SQL

Scikit-Learn

XGBoost

LightGBM

Docker

GitOps

LangChain

LangGraph

Tools

Databricks

MLflow

Sagemaker

AWS

Azure

Faiss

OpenSearch

Job description

We are looking for anaccomplishedLeadData Scientistwith10+ Year of experience and havingdeep expertise&hands-oninclassical machine learning,GenAIApplications &MLlifecycle,

Key Responsibilities

1. Machine Learning & Statistical Modelling

• Build andoptimizecomplex ML models: regression, classification, clustering, sequence models, time series forecasting.
• Lead sophisticated feature engineering and data quality analysis.
• Apply statistical modelling techniques, experimental design, andPerformance evaluation.
• Develop scalable and maintainable ML pipelines for structured and unstructured data.

2. GenAI & LLM Systems

• Architect and develop LLM-based applications usingSOTA LLMs.
• Build RAG pipelines using vector databases (faiss,aisearch,opensearch, PG vector etc).
• Integrate GenAI systems with enterprise apps, APIs, and data sources.
• Model Context Protocol (MCP) & Tooling
• Exposure of Agentic systems and multi-agent workflows

3.Agentic Systems & Model Context Protocol (MCP)

• Exposure to agentic system design, includingtoolcallingworkflows, planner–executor patterns, and multiagent coordination.
• Integrate memory architectures such as episodic, semantic, andvectorbasedlongtermmemory within agent workflows.
• Implement and manage Model Context Protocol (MCP) servers to enable seamless connectivity between LLMs, tools, APIs, and enterprise applications.
• Collaborate with engineering teams to build reliable, extensible agent tooling and ensure smooth integration into production environments.

4.Cloud ML-Ops & Quality

• ML Modelling,data drift, concept drift, model quality monitoring.
• Handson experience across AWS/Azure/Databricks, with flexibility to work on any cloudplatform.
• Adhere to stringent quality assurance and documentation standards using version control and code repositories (e.g., Git, GitHub, Markdown)

5. Leadership & Collaboration

• Lead technical direction for AI solutions.
• Work with product teams to define AIfeatures.

Required Skills & Experience

• 10+ yearsinClassicalML,GenAI & ML-Ops.
• Strong experience in:
• Python,PySpark,SQL, Scikit-Learn,XGBoost,LightGBM, Random Forest
• LangChain,LangGraph,LangSmith(tracing, metrics, evaluations)
• MLflow/Sagemaker/ Databricks
• Docker, Git-Ops
• Experience building production-grade GenAIapplications.
• Skilled in EDA, DOE, and model evaluation metrics foridentifyingdata patterns,validatinghypotheses, and improving model quality