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.