What you'll do :

• Build AIML models that serve for content generation, content recommendation, and content moderation using LLMs.
• Responsible for converting business requirements into robust data science algorithms.
• Responsible for providing technical leadership to a team of data science engineers.
• Responsible for design, development, deployment of AI/ML, NLP, statistical & content recommendation algorithms that may not require LLMs.
• Leverage our rich data sets of user data to perform research, develop models and create data products with our Development & Product teams
• Develop novel and scalable data systems in cooperation with our system architects that leverage datasets using machine learning techniques to enhance the user experience
• Collaborates effectively with cross functional teams to deliver end-to-end products & features.
• Demonstrates ability to multi-task and re-prioritize responsibilities based on changing requirements.
• Responsible for creating model validation framework.
• Leading a data science team that delivers actionable results through the combination of data science solutions, business skills, statistical knowledge, and understanding of data.
• Distilling complex analytical results into presentable, digestible, and actionable feedback for product and engineering teams.
• Recruit, build and retain a top-tier data science team that brings together top talent at all levels, cutting edge technology, and analytic innovation.
• Assess, inspire and motivate a high performing team of data science professionals.

What makes you a great fit :

• Proven experience in solving open-ended business problems using data-science/ML/AI from initial ideation stage up to delivering the ready-to-use solution.
• Strong Programming experience in Python. Familiar with various Machine Learning algorithms and Statistical methods.
• 10+ yrs of working experience in MLData science with NLP background
• Must have delivered multiple data science product(s) in production. Deployment with LLMs is a plus.
• Experience designing and implementing ML systems with Data Pipelines (Data Cleaning and creative Feature Engineering), Model Iterations and Evaluation, High throughput ML(Model Training, Evaluation, GPU vs CPU, XGBoost ML), Python ML Libs(TensorFlow, PyTorch).
• Experience in leading a team of 3-5 data scientists.
• Experience using tools to deploy models into the production environment such as Jenkins
• Experience with Cloud infrastructure and platforms (Azure, AWS)
• Experience with software stack components including common programming languages, back-end technologies, database modelling, continuous integration, services oriented architecture, software testability etc.