What your average day would look like :

- Collaborate with product and engineering teams to understand requirements and devise possible solutions.

- Explore existing research papers, ideas and codebases that can be leveraged in current tasks.

- Search for open source datasets and/or design synthetic data pipelines (including data augmentation).

- Devise and implement experiments using DL/ML models.

- Evaluate the experiments to find failure patterns and come up with improvements in data/model architecture/loss function etc.

- Communicate results and ideas to key stakeholders.

- Optimize the models for production and collaborate with software engineers for deployment.

Must have skills :

- Hands-on experience in dealing with image data and CNN based architectures.

- Should have worked on deep learning frameworks (like pytorch, tensorflow, keras etc.

- Proficient in Python and packages like Numpy, Pandas, OpenCV.

- Good understanding of data structures and algorithms along with OOPS, Git, SDLC.

- Mathematical intuition of ML and DL algorithms.

- Good understanding of Statistics, Linear Algebra and Calculus.

- Should be able to perform thorough model evaluation by creating hypotheses on the basis of statistical analyses.

Highly desired :

- Hands on experience with latest computer vision model architectures and concepts like ViTs, GANs, Diffusion, Vision Language Models.

- Knowledge of training and inference optimizations using CUDA, C++, ONNX, TensorRT, OpenVino etc and profiling of ML pipelines.

- Worked on building production level APIs for serving models (Flask, Django, TF Serving).

- Hands-on experience of using MLOps tools.

- Lead and mentor a team of junior data scientists and analysts, providing technical guidance, code reviews, and career development support.

- Oversee the end-to-end delivery of data science projects by coordinating with cross-functional teams and ensuring alignment with business goals.

- Manage a team of data professionals, fostering a collaborative and innovative work environment to drive analytics excellence.

- Act as a technical lead in projects, taking ownership of team deliverables, timelines, and quality assurance of data models and analytics solutions.

- Facilitate regular team meetings, set goals and priorities, and monitor progress to ensure efficient execution of data-driven initiatives.

- Collaborate with product managers, business stakeholders, and engineering teams while managing a high-performing team of data scientists.

- Champion best practices in data science, model development, and deployment while promoting a culture of continuous learning within the team.