Vice President, GenAI Technology

Fidelity Investments•Durham, NC

1d•$140,000 - $285,000

About The Position

The Applied GenAI Technology team within Fidelity's Asset Management Technology is seeking a motivated data scientist with proven experience in GenAI-related projects, machine learning, and data science. This individual contributor role focuses on leading GenAI projects with diverse scope and complex business and technical challenges. The role involves coordinating with senior business and technology partners to develop solutions for complex business analytical needs, overseeing the end-to-end process of moving code from research to production, and delivering results with clear, measurable business impact. Responsibilities include consulting with senior partners to identify priorities and establish analytic goals, executing data identification, collection, and qualification activities, and presenting findings to senior technical and non-technical audiences. The ideal candidate enjoys collaboration, can execute multiple initiatives in parallel, and plays an active mentorship role. They should have a strong desire to create a climate that values contributions, drive, ownership, initiative, and achievement of results, along with excellent planning, project management, communication, leadership, and research skills. Experience communicating results to business stakeholders, focusing on clear and understandable delivery including conveying statistical findings through data visualizations, is essential. An enthusiasm for learning new skills and domains, including applying state-of-the-art ML research and LLMs to real-world data challenges, is also expected.

Requirements

PhD or Master's in Data Science, Computer Science, Statistics, Physics, or Finance (with a background in Statistics), with 7 plus years of industrial experience.
Experience working with LLMs for solving data science problems, information retrieval applications, clustering, and coding; emphasis on putting (or working with) LLM-based applications in production.
Experience working and closely collaborating with an Engineering team, sharing best development practices under a common goal of shipping reliable software.
Deep expertise in Python, as well as data-centric techniques including engineering principles for building efficient inference tools.
Experience taking an application from research to production and realizing measurable value from it to the team or firm.
Ability to work on and drive progress for multiple projects or initiatives at the same time.
Excellent planning, project management, communication, leadership, and research skills.

Nice To Haves

Enjoys collaboration and revels in working as part of a team to solve deep applied problems.
Plays an active mentorship role to others in the team.
Enjoys learning new skills and appreciates the challenges that come from working with state of the art AI models in a rapidly evolving space.

Responsibilities

Leads GenAI projects with diverse scope and complex business and technical challenges.
Coordinates with senior business and technology partners to develop solutions to the most complex business analytical needs.
Oversees end-to-end process to push code from research to production.
Delivers results with clear and measurable impact to the business.
Consults with senior business and technology partners to identify priorities and establish analytic goals, as well as form alignment on next steps.
Executes on direction for data identification, collection and qualification activities.
Presents reports and findings to senior technical and non-technical audiences.
Enjoys collaboration and revels in working as part of a team to solve deep applied problems.
Able to execute on multiple initiatives in parallel.
Plays an active mentorship role to others in the team.
Guides business on identifying AI/ML use cases and optimally contributing to brainstorming sessions or consulting sessions.
Creates a climate that values and rewards contributions, drive, ownership, initiative, and achievement of results.
Communicates results to business stakeholders with a focus on clear, concise, and understandable delivery including conveying statistical findings through data visualizations.
Applies state-of-the-art ML research and LLMs to real-world data challenges.