Lead Data Scientist- ML

Role & Responsibilities

• Experience of working on end-to-end data science pipeline with the ability to clearly design efficient data models, training data samples, training pipelines and inference pipelines to ensure reproducibility.
• Strong experience in evaluating different types of AI/ML outputs and models (recommender systems, similarity scoring, classification and regression).
• Should be able to break down complex business problems into sub-tasks that can be solved with AI/ML to enable decision making at scale. Should have good grasp on statistical concepts and empirical methods that drive post-modeling decisions like sorting, ranking, selection etc.
• Should have good experience in converting PoCs and experimented models into production grade AIML systems, have the ability to collaborate effectively with ML engineering and MLOPs teams to take AI/ML models to production, build and serve models through APIs and support integration of AI/ML models into web-aps.
• Ability to lead mid-sized teams, plan project tracks around AI/ML and data science and balance delegation and hands-on development.
• Experience in taking first principles approach to solving data science problems, understand and communicate data requirements to data engineering teams and explain model outputs to business users.
• Ability and experience in engaging business users to understand requirements and translate them into technical data science problems. Be exceptional at communicating and translating model algorithms and their outputs to non-technical users and stakeholders.
• Fluent in agile ways of working and agile project management practices. Should be comfortable to create efficient user stories, and other agile project artifacts.