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Lead Data Scientist
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This role is for one of our clients
Industry: Software Development
Seniority level: Associate level
Min Experience: 1 years
Location: Mumbai
JobType: full-time
We’re looking for an impact-driven Machine Learning leader to own and scale applied data science initiatives that directly influence business outcomes. This role blends hands-on model development, production-grade ML deployment, and team leadership, making it ideal for someone who enjoys turning ambiguous business problems into scalable, real-world ML systems.
You’ll lead multiple ML initiatives end-to-end—defining problems, shaping data strategy, building models, deploying solutions, and ensuring measurable impact—while mentoring a growing team and strengthening ML foundations across the organization.
What You’ll Do
Business-Focused ML Ownership
Partner with product, business, and engineering teams to translate real-world challenges into ML-ready problem statements.
Define success metrics and ensure models deliver measurable business value.
Prioritize ML initiatives based on impact, feasibility, and scalability.
Model Development & Applied Data Science
Lead data exploration, feature engineering, and model development using classical ML and advanced techniques.
Select appropriate algorithms and optimize models for performance, robustness, and interpretability.
Drive experimentation cycles and evidence-based decision-making.
Production ML & MLOps
Design and maintain production-ready ML pipelines from training to deployment and monitoring.
Implement reusable MLOps frameworks, CI/CD workflows, and experiment tracking.
Deploy models using containerization and API-based services (Docker, FastAPI, etc.).
Ensure reliability, scalability, and maintainability of deployed ML systems.
Data Pipelines & Automation
Build and manage scalable data pipelines using orchestration and distributed processing tools.
Ensure data quality, reproducibility, and efficient data access for modeling workflows.
Collaborate with data engineering teams on pipeline optimization.
Team Leadership & Mentorship
Mentor and guide a team of data scientists, helping them grow technically and analytically.
Review work for quality, rigor, and clarity.
Foster a culture of experimentation, ownership, and continuous improvement.
Communication & Influence
Present insights, model outcomes, and trade-offs clearly to both technical and non-technical stakeholders.
Enable leadership to make data-informed decisions through clear storytelling and visualization.
What Will Help You Succeed
Experience & Technical Depth
4–5 years of hands-on experience in Machine Learning or Applied Data Science.
Strong grounding in statistics, probability, optimization, and ML fundamentals.
Proficiency in Python and libraries such as NumPy, pandas, scikit-learn, XGBoost, etc.
Experience deploying ML solutions in real-world production environments.
Hands-on exposure to cloud platforms (AWS, GCP, or Azure).
Engineering & Platform Skills
Experience with orchestration or distributed systems (Airflow, Spark, or similar).
Familiarity with Docker, APIs, and production ML patterns.
Understanding of CI/CD, monitoring, and ML lifecycle management.
Leadership & Mindset
Ability to balance technical depth with business context.
Comfortable owning outcomes in fast-moving, ambiguous environments.
Strong communication skills and stakeholder empathy.
Enjoy mentoring others and scaling both systems and teams.
Nice to Have
Experience with LLMs, GenAI applications, or real-time ML systems.
Contributions to open-source projects or research-driven initiatives.
Exposure to startup or high-growth environments.
Why Join
High ownership role with visibility into business impact.
Opportunity to shape ML standards, tooling, and culture.
Balance of hands-on problem solving and people leadership.
Clear growth path into senior ML leadership or platform ownership roles.
Core Skills
Applied Machine Learning · Data Science · Python · MLOps · Model Deployment · Data Pipelines · Airflow · Spark · Docker · FastAPI · Cloud ML · Team Leadership · Analytics Strategy
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
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