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.