Lead Data Scientist

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.