Data Science Manager

Role:

Experience:

Location: Bengaluru/

Compensation: 38-40 LPA Based on experience

Role Overview:

Data Science Manager

Key Areas of Responsibility

Team Leadership and Management

• Lead and mentor a team of data scientists, fostering a culture of collaboration, innovation, and accountability.
• Provide day-to-day technical guidance, career development, and performance feedback to team members.
• Take full ownership of the

  mentorship and management

  of the data science team.

Machine Learning and AI Innovation

• Design and implement scalable ML/DL models to support program delivery and engagement.
• Build and manage infrastructure to

  serve predictions at scale

  , ensuring reliability and performance.
• Drive adoption of

  generative modeling

  and large language model (LLM) use cases relevant to education.

MLOps and Deployment

• Collaborate with engineering and product teams to deploy ML models using

  FastAPI, Docker, Kubernetes

  .
• Own and improve

  CI/CD pipelines

  for model development and deployment.
• Ensure model performance monitoring and retraining workflows are in place

Strategic Collaboration

• Work with analytics, product, and engineering to identify high-leverage ML opportunities.
• Stay ahead of emerging AI trends and translate them into actionable innovations for the organization.

Responsibilities in Detail

• Develop and refine machine learning models for real-time and batch prediction.
• Implement deep learning solutions using frameworks like

  PyTorch

  or

  TensorFlow

  .
• Fine-tune and evaluate

  large language models

  for use in community engagement, content generation, and personalization.
• Optimize model deployment pipelines to ensure low latency and high throughput.
• Integrate ML solutions into our product and data infrastructure.
• Maintain strong documentation, reproducibility, and experiment tracking.
• Champion ethical and inclusive AI practices in educational applications

Critical Success Factors

Technical Expertise:

• Strong understanding of

  machine learning, deep learning,

  and

  generative AI concepts

  .
• Proven experience with

  PyTorch

  or

  TensorFlow

  for model development.
• Hands-on experience with

  FastAPI

  ,

  Docker

  , and

  Kubernetes

  for model deployment.
• Familiarity with

  MLOps

  , CI/CD, and infrastructure for scaling AI systems.
• Experience with

  LLM fine-tuning

  and model serving at scale.

• Strategic Thinking:

• Ability to translate organizational needs into practical AI solutions.
• Strong judgment around trade-offs between accuracy, interpretability, and scalability.

• Collaboration Skills:

• Excellent communication skills to work with technical and non-technical stakeholders.
• Comfort working in a fast-paced, mission-driven, and agile environment.