Data Science Manager (Machine Learning)

Role Summary

We are looking for a technically strong and visionary Data Science Manager / Senior Manager to lead a team of data scientists and ML engineers in building scalable, production-grade AI solutions. This role demands deep expertise in machine learning, hands-on technical leadership, and the ability to architect and guide complex data science initiatives from ideation to deployment.

Key Responsibilities

Technical Leadership

• Architect and oversee the development of advanced ML models including supervised, unsupervised, and deep learning approaches.
• Drive best practices in model development, validation, deployment, and monitoring.
• Lead code reviews, model audits, and ensure reproducibility and scalability of solutions.
• Champion ML Ops practices and CI/CD pipelines for ML workflows.

Team Management

• Mentor and grow a team of data scientists and ML engineers.
• Foster a culture of technical excellence, ownership, and continuous learning.
• Set clear goals, provide feedback, and support career development.

Strategic Execution

• Translate business problems into data science solutions with measurable impact.
• Prioritize projects based on ROI, feasibility, and strategic alignment.
• Collaborate with cross-functional teams including engineering, product, and domain experts.

Innovation & Thought Leadership

• Stay ahead of the curve on emerging ML techniques, tools, and frameworks.
• Evaluate and integrate cutting-edge technologies into the teams workflow.
• Represent the data science function in technical forums and leadership discussions.

Required Qualifications

• Expertise in Computer Science, Machine Learning, Statistics, or related field. Min Graduate
• 8+ years of experience in data science, with 3+ years in a technical leadership role.
• Proven track record of deploying ML models in production environments.
• Strong programming skills in Python (preferred), R, or Scala.
• Deep understanding of ML frameworks (e.g., PyTorch, TensorFlow, XGBoost, scikit-learn).
• Experience with cloud platforms (AWS, GCP, Azure) and containerization (Docker, Kubernetes).
• Familiarity with MLOps tools (MLflow, Airflow, SageMaker, etc.).

Education: Preferably From Mathematics and Statistics background

Location: Hyderabad

Work mode: Hybrid

Shift time: 2 PM to 11 PM

Thanks & Regards,