Data Science Solution Architect

Title: Data Scientist 2

Skills: OpenCV, Pytorch / Tensorflow, SciPy stack, Machine Learning, Deep Learning

Years of Experience: 2 years to 6 years

Description:

The Center of Data for Public Good (CDPG) is an interdisciplinary research center at the Indian Institute of Science, Bangalore, focusing on Data Science for translational research in domains such as Traffic & Transportation, Air Quality, Geospatial Analytics, Agriculture, and Health Care. We are building a Digital Twin for mobility by combining many data sources such ANPR, telco, AVLS, and incident data with key measurements such as vehicle counts and speeds extracted from video feeds from surveillance cameras. These measurements will be used in building a mobility model which can predict traffic congestion and anomalies, and also support short-term and long-term scenario analysis for use cases such as road closures, major events and infrastructure planning. As part of this exciting project, we are looking for experienced Data Scientists with good experience in building Computer Vision applications.

Responsibilities

Develop Machine Learning and AI models to extract insights, detect patterns, and generate predictive signals from large-scale, heterogeneous datasets

Design and build models for tasks such as forecasting, anomaly detection, classification, clustering, and optimization

Work on large-scale data processing and model training pipelines across distributed systems

Optimize models for performance, scalability, and efficiency (memory, latency, throughput)

Deploy models into production-grade inference systems and ensure reliability at scale

Build and maintain end-to-end MLOps pipelines including experiment tracking, model versioning, hyperparameter tuning, and monitoring

Stay up-to-date with the latest research and translate state-of-the-art methods into practical, scalable solutions

Good to see on your resume:

Experience with a wide range of Machine Learning models (tree-based models, deep learning, probabilistic models, etc.) and their deployment at scale

Hands-on experience with frameworks such as TensorFlow, PyTorch, and distributed computing tools

Familiarity with MLOps tools like MLflow, Kubeflow, Ray, or Dask

Strong foundations in machine learning, linear algebra, probability, and statistics

Experience working with large-scale datasets and distributed data processing systems

Ability to read, understand, and implement research papers in Machine Learning / AI

Proficiency in Python, with experience in multiprocessing, asynchronous programming, and performance optimization

Exposure to data engineering concepts such as pipelines, data modeling, and system design