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.

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