Lead Data Scientist

Overview

Lead the design, development, deployment, and operationalization of production-grade machine learning and advanced analytics solutions. Guide ML solution architecture, MLOps practices, technical standards, and mentoring across the team.

What you'll do

• Lead machine learning model design, development, evaluation, and deployment.
• Translate business and product requirements into analytical approaches, feature sets, evaluation methods, and deployment plans.
• Build reusable pipelines for data preparation, feature engineering, training, validation, tuning, and model packaging.
• Implement MLOps practices including experiment tracking, versioning, CI/CD, monitoring, drift detection, retraining, and rollback readiness.
• Partner with engineering teams to integrate models into applications, APIs, workflows, and business systems.
• Establish production monitoring for accuracy, drift, latency, stability, explainability, and business outcomes.
• Mentor data scientists and ML engineers on modeling rigor and production readiness.

What you'll need

• 8+ years of experience in data science, machine learning, applied AI, or advanced analytics.
• Hands-on experience delivering production-grade machine learning models.
• Strong expertise in supervised and unsupervised learning, feature engineering, validation, tuning, and performance interpretation.
• Strong Python skills and experience with common ML and data science libraries.
• Experience building end-to-end ML pipelines and applying MLOps practices.
• Experience with SQL, batch pipelines, streaming data, APIs, and application services.
• Ability to guide technical decisions, explain trade-offs, and mentor other data scientists or ML engineers.

Nice to have

• Experience leading complex ML initiatives involving multiple models and cross-functional teams.
• Experience with forecasting, optimization, recommendation systems, anomaly detection, NLP, or applied AI.
• Familiarity with LLM-assisted analytics, embeddings, retrieval-enhanced workflows, or hybrid ML and GenAI solutions.
• Experience with feature stores, explainability frameworks, responsible AI toolkits, and model governance.
• Working knowledge of distributed training, large-scale data processing, and production performance optimization.

Details

• Location: Bangalore, Karnataka, India.