JPMorgan Data Science Associate 

India, Karnataka, Bengaluru 

185263639

Job responsibilities

• Organizes, updates, and maintains gathered data that will aid in making the data actionable.Work with product managers, data scientists, ML engineers, and other stakeholders to understand requirements. Design, develop, and deploy state-of-the-art AI/ML/LLM/GenAI solutions to meet business objectives.
• Demonstrates basic knowledge of the data system components to determine controls needed to ensure secure data access.Develop and maintain automated pipelines for model deployment, ensuring scalability, reliability, and efficiency. Conduct thorough evaluations of generative models (e.g., GPT-4), iterate on model architectures, and implement improvements to enhance overall performance in NLP applications.
• Be responsible for making custom configuration changes in one to two tools to generate a product at the business or customer request
• Updates logical or physical data models based on new use cases with minimal supervision.Stay informed about the latest trends and advancements in the latest AI/ML/LLM/GenAI research, implement cutting-edge techniques, and leverage external APIs for enhanced functionality.
• Adds to team culture of diversity, equity, inclusion, and respect

Required qualifications, capabilities, and skills

• Formal training or certification on Data Science concepts and 2+ years applied experience
• Experience in applied AI/ML engineering, with a track record of developing and deploying business critical machine learning models in production.
• Proficiency in programming languages like Python for model development, experimentation, and integration with Azure OpenAI API.
• Experience with machine learning frameworks, libraries, and APIs, such as TensorFlow, PyTorch, Scikit-learn, and Langchain/Llamaindex.
• Experience with agile methodologies such as CI/CD, Application Resiliency, and Security
• Experience with cloud computing platforms (e.g., AWS, Azure, or Google Cloud Platform), containerization technologies (e.g., Docker and Kubernetes), and microservices design, implementation, and performance optimization.
• Solid understanding of fundamentals of statistics, machine learning (e.g., classification, regression, time series, deep learning, reinforcement learning), and generative model architectures.
• Ability to identify and address AI/ML/LLM/GenAI challenges, implement optimizations and fine-tune models for optimal performance in NLP applications.
• Strong collaboration skills to work effectively with cross-functional teams, communicate complex concepts, and contribute to interdisciplinary projects.