Data Scientist

Tricog Health; Bengaluru; On-site; INR 1,500,000 - 2,800,000; Full time

Job summary

Tricog Health is seeking a curious Data Scientist to develop AI models that analyze cardiac data and improve patient outcomes. You will build end-to-end pipelines, deploy models, and collaborate with engineers, clinicians, and researchers to deliver high-impact solutions.

The role emphasizes MLOps, cloud deployment, and staying current with deep learning research, including medical image analysis and time-series data.

Qualifications

• MS/M.Tech preferred; strong BTech with relevant experience considered.
• Proficiency in Python and core data science libraries (NumPy, pandas, scikit-learn).
• Hands-on experience with PyTorch and other major DL frameworks.
• Familiarity with MLOps basics, including containerization (Docker) and cloud services (AWS/Azure/GCP).
• Understanding of model evaluation metrics, cross-validation, and debugging ML systems.

Responsibilities

• Design and implement ML/DL models for cardiac datasets.
• Develop robust data pipelines for ingestion, cleaning, feature engineering, labeling.
• Deploy models to production and monitor performance; build REST APIs (FastAPI/Flask).
• Design scalable MLOps practices with CI/CD and model monitoring on cloud platforms.
• Optimize inference speed and resource efficiency for deployment across platforms.
• Collaborate with engineers, clinicians, and researchers to translate requirements.
• Document models, experiments, and research; stay updated on DL and time-series methods.

Skills

Python

NumPy

pandas

scikit-learn

PyTorch

MLOps

Model evaluation

Education

MS/M.Tech preferred

BTech with relevant experience

Tools

Docker

AWS

Azure

GCP

FastAPI/Flask

Job description

The Role:

We are looking for a curious and passionate Data Scientist to join our high-impact team. You’ll work directly on AI models that analyse cardiac data and further save life. This role offers the unique opportunity to see your work make a tangible difference in patient outcomes while building state-of-the-art models.

What You’ll Do:

• Design and Implement AI Models: Develop, train, and evaluate machine learning and deep learning models for cardiac datasets and associated metadata.
• Data Pipeline Development: Work with large, complex, and sometimes messy clinical datasets. Contribute to building robust and scalable data pipelines for data ingestion, cleaning, feature engineering, and labeling.
• Model Deployment and MLOps:Deploy models to production environments and monitor their performance in real-world clinical settings. Build and maintain REST APIs for model inference using frameworks like FastAPI or Flask. Design scalable API endpoints with proper request validation, error handling, and and maintain robust MLOps practices, including version control, continuous integration/continuous deployment (CI/CD), and model monitoring in a production environment (e.g., cloud platforms like AWS, Azure, or GCP).
• Performance Optimization: Optimize model performance for inference speed and resource efficiency, crucial for deployment on various platforms (cloud, edge devices).
• Collaboration: Work collaboratively with software engineers, data scientists, and clinical domain experts to translate clinical needs into technical requirements and deliver high-impact solutions.
• Documentation and Research: Maintain detailed documentation of models, code, and experiments. Stay current with the latest research in deep learning, medical image analysis, and time-series analysis.
• Regulatory and Compliance : Develop documentation in order to comply with regulatory requirements such as CDSCO, FDA etc.

What We’re Looking For:

Required:

• Experience: 2+ years of professional experience as a Data Scientist, or a related role.
• Education: MS/M.Tech preferred; strong BTech with relevant experience considered.
• Programming: Proficiency in Python and experience with core data science libraries (NumPy, pandas, scikit-learn).
• Deep Learning Frameworks: Hands-on experience with at least major deep learning framework (PyTorch).
• MLOps Basics: Familiarity with MLOps principles, including containerization (Docker) and cloud service experience (AWS, Azure, or GCP).
• Understanding of model evaluation metrics, cross-validation, and debugging ML systems.
• Ability to read and implement research papers.

Preferred Skills

• Experience with healthcare data.
• Understanding of statistical methods and experimental design for model validation.
• Experience with structured training pipelines such as with Pytorch Lightning.
• Knowledge of regulatory requirements for medical devices (FDA, CE marking).
• Experience with cloud platforms (AWS, GCP, Azure) and serverless deployments.
• Publications in top medical journals/conferences such as ICLR, Neurips, JAMA Cardiology, EHJ, MICCAI etc.