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.