Lead Data Scientist

Sourcebae; Chennai; Hybrid; INR 3,000,000 - 5,500,000; Full time

Job summary

Sourcebae seeks a Lead Data Scientist in hybrid mode across Chennai/Coimbatore/Bangalore/Pune to own the ML lifecycle end-to-end and guide a small team of data scientists and engineers. The role demands deep hands-on experience in classical ML, DL, GenAI, and MLOps, with strong client-facing and architectural decision-making skills.

The candidate will set technical direction, defend model choices, and drive production pipelines, CI/CD, and monitoring across diverse cloud platforms.

Qualifications

• 8+ years of Data Science experience.
• Hands-on expertise in classical ML including regression, classification, XGBoost and LightGBM.
• Deep learning experience with scaling architectures.
• Expertise in NLP, forecasting, recommendation systems or CV.
• Snowflake hands-on experience for data/training decisions.
• Production-scale feature engineering and ML pipelines.
• Distributed training with Spark or Ray.
• Ownership of production ML models with monitoring and retraining.
• Strong leadership and client-facing communication.
• Experience with Azure ML, Databricks, SageMaker or Vertex AI.
• GenAI/LLM experience beyond prototypes.
• MLOps with MLflow and CI/CD.

Responsibilities

• Own the complete model lifecycle from problem framing to retraining and deployment.
• Defend model architectures between classical ML and deep learning.
• Design experiments including A/B testing and causal inference.
• Build production-grade feature pipelines and training infra.
• Work with Spark, Ray or equivalent distributed computing.
• Monitor models for drift and decide retraining or rollback.
• Set technical standards for modeling, reviews and validation.
• Mentor 2–4 Data Scientists/Engineers.
• Serve as primary technical contact for clients.
• Explain trade-offs and limitations to non-technical stakeholders.
• Drive GenAI/LLM solutions including retrieval design and evaluation.
• Implement MLOps with model registries and automated retraining.

Skills

Python

SQL

Snowflake

XGBoost

LightGBM

NLP

Forecasting

Recommendation

Computer Vision

Azure ML

Databricks

SageMaker

Vertex AI

MLOps

MLflow

GenAI/LLM

A/B Testing

Causal Inference

Uplift Modeling

Spark

Ray

Tools

Snowflake

Azure ML

Databricks

SageMaker

Vertex AI

MLflow

Spark

Ray

Job description

Position: Lead Data Scientist

Experience: 8+ Years

Location: Chennai / Coimbatore / Bangalore / Pune – Hybrid

Required Skills

• Classical ML – XGBoost, LightGBM, Regression, Classification
• Deep Learning – NLP / Forecasting / Recommendation / Computer Vision
• Snowflake – Mandatory, genuine hands‑on platform experience
• Python & SQL
• Azure / Azure ML or similar cloud ML platforms
• Databricks / SageMaker / Vertex AI or equivalent
• MLOps – MLflow/model registry, CI/CD, automated retraining
• Spark / Ray or equivalent distributed computing
• GenAI / LLM – RAG, evaluation, monitoring and failure‑mode analysis
• A/B Testing, Causal Inference, Uplift Modeling

Role Overview

We are looking for a Lead Data Scientist who can set the technical direction for a workstream, make architecture and modeling decisions, and lead a small team of Data Scientists/Engineers.

The candidate should have strong hands‑on experience across the complete ML lifecycle and be capable of acting as the senior technical authority for client‑facing discussions.

Key Responsibilities

• Own the complete model lifecycle from problem framing and feature engineering through training, validation, deployment, monitoring and retraining.
• Select and defend appropriate model architectures, including decisions between classical ML and deep learning.
• Design and execute rigorous experiments including A/B testing, causal inference and uplift modeling.
• Build production‑grade feature pipelines and training infrastructure.
• Work with distributed computing frameworks such as Spark, Ray or equivalent.
• Monitor production models for drift and degradation and determine retraining, redesign or rollback strategies.
• Establish technical standards for modeling, code reviews, experiment tracking and model validation.
• Mentor and technically guide 2–4 Data Scientists/Engineers.
• Act as the primary technical point of contact for clients.
• Explain model trade‑offs, limitations, assumptions and failure modes to non‑technical stakeholders.
• Drive GenAI/LLM solutions including retrieval design, evaluation frameworks and production reliability.
• Implement MLOps practices including model registries, automated retraining and ML CI/CD.

Must-Have Requirements

• 8+ years of Data Science experience.
• Strong hands‑on expertise in classical ML including regression, classification, XGBoost and LightGBM.
• Strong deep learning experience with architecture selection and training at scale.
• Deep expertise in at least one area: NLP, Forecasting, Recommendation Systems or Computer Vision.
• Genuine hands‑on Snowflake experience, including architecture and data/training decisions.
• Production‑scale feature engineering and ML pipelines.
• Distributed training / large‑scale compute experience using Spark, Ray or equivalent.
• Proven ownership of production ML models, including monitoring, drift detection, retraining and rollback.
• Strong technical leadership and mentoring experience.
• Excellent client‑facing communication and ability to defend technical decisions.
• Hands‑on experience with cloud ML platforms such as Azure ML, Databricks, SageMaker or Vertex AI.
• Practical GenAI/LLM experience beyond demo/prototype projects.
• Strong MLOps experience with MLflow or similar tools, automated retraining and ML CI/CD.
• Consulting/professional services experience with the ability to manage multiple client engagements.

Primary Technical Skills

Python | SQL | Snowflake | XGBoost | LightGBM | Regression | Classification | Deep Learning | NLP | Azure | Azure ML | Databricks | Spark/Ray | MLOps | MLflow | GenAI/LLM | RAG | A/B Testing | Causal Inference | Uplift Modeling