Applied AI Lead Data Scientist - Vice President

JPMorgan Chase-posted 3 months ago

$164,350 - $260,000/Yr

Full-time

Jersey City, NJ

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Join us as we revolutionize data and analytics with cutting-edge Generative AI, shaping the future of our organization. Experience career growth, collaborate with top talent, and make a meaningful impact through ethical and sustainable AI practices. As a Generative AI Data Science Lead in the Applied Solutions Team, you will drive the development and optimization of LLM-aided AI products. You work closely with cross-functional teams to deliver scalable solutions that support our business objectives and foster innovation. You will help shape the future of our organization by leveraging advanced data science and engineering practices. You will collaborate with the ML Centre of Excellence, AI Research, and Engineering teams to design and deliver high-impact GenAI products and APIs. Your expertise will ensure our solutions are robust, scalable, and aligned with the needs of our business and stakeholders.

Combine vast data assets with advanced AI, including LLMs and Multimodal LLMs
Bridge scientific research and software engineering, applying expertise in both domains
Collaborate with engineering teams to lead the design and delivery of GenAI products
Architect and implement scalable AI Agents, Agentic Workflows, and GenAI applications
Integrate GenAI solutions with enterprise platforms using API-based methods
Establish validation procedures with Evaluation Frameworks, bias mitigation, safety protocols, and guardrails
Align ML problem definition with business objectives
Communicate technical information and ideas effectively to stakeholders

PhD in a quantitative discipline such as Computer Science, Mathematics, or Statistics
Ten years of experience in an individual contributor role in ML engineering
Strong understanding of statistics, optimization, and ML theory, focusing on NLP and/or Computer Vision algorithms
Demonstrated experience in parameter-efficient fine-tuning, model quantization, and quantization-aware fine-tuning of LLM models
Experience integrating GenAI solutions with enterprise platforms via standardized API patterns
Ability to establish validation procedures, including Evaluation Frameworks, bias mitigation, safety protocols, and guardrails
Excellent grasp of computer science fundamentals and SDLC best practices
Strong communication skills to build trust with stakeholders

Experience designing and implementing pipelines using DAGs such as Kubeflow, DVC, or Ray
Ability to construct batch and streaming microservices exposed as gRPC or GraphQL endpoints
Hands-on experience implementing distributed, multi-threaded, and scalable applications using frameworks such as Ray, Horovod, or DeepSpeed

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