We are looking for a Senior Data Scientist with 5-8 years of experience to lead the development and deployment of advanced machine learning and deep learning solutions. The ideal candidate should be hands-on with Python, PyTorch, and modern ML practices, and must have experience in technically mentoring a team of data scientists.
As a Senior Data Scientist, you will work closely with cross-functional teams to deliver end-to-end AI/ML projects and ensure scalable, maintainable solutions.

Key Responsibilities:

Lead and contribute to the design, development, and deployment of machine learning and deep learning models.Collaborate with product managers, software engineers, and stakeholders to define project goals and deliverables.Ensure reproducibility, scalability, and maintainability of ML pipelines.Translate business requirements into well-architected data science solutions.Mentor and provide technical guidance to a team of data scientists and ML engineers.Conduct code reviews, encourage best practices in ML and software engineering.Develop and maintain model training pipelines using Docker, Git, and CI/CD practices.Communicate findings and recommendations through presentations and technical documentation.

Must-Have Skills:

Programming: Proficient in Python with efficient usage of supporting libraries like numpy, pandas, etc. Hands-on experience in ML/DL frameworks like Scikit-learn, PyTorch.Machine Learning & Deep Learning: Strong understanding of supervised/unsupervised learning, neural networks, computer vision, and model evaluation, Fundamentals of Statistics, Designing models.Data Handling: Solid experience with SQL and working with structured/unstructured data.Tools & Platforms: Familiarity with Git, Docker, and Linux environments.Leadership: Demonstrated experience in technically leading and mentoring a team of data scientists or ML engineers.Knowledge of latest state of the art in AI

Good to Have:

Exposure to MLOps tools like MLflow, or similar.Experience working with vision, image data.Experience with cloud platforms (AWS, GCP, or Azure).Contributions to open-source projects or research publications.Knowledge of data engineering practices and distributed computing frameworks like Spark.