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Job Description & How to Apply Below
Lead Data Scientist
Location :
Hyderabad.
Department :
Data Science & AI
Reporting To : AVP / Director of Data Science
Role Summary
We are looking for a technically strong and visionary Lead Data Scientist 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 MLOps 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 team’s workflow.
Represent the data science function in technical forums and leadership discussions.
Required Qualifications
Exerptise 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, Tensor Flow, XGBoost, scikit-learn).
Experience with cloud platforms (AWS, GCP, Azure) and containerization (Docker, Kubernetes).
Familiarity with MLOps tools (MLflow, Airflow, Sage Maker, etc.).
Regards,
Shaik Thanveer Ahamed,
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