Lead Data Scientist

Position:

Experience:

Location:

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

Apply Now

Khushboo@Sourcebae.com

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