Generative AI & NLP Data Scientist - LLMs & KG

McKinsey & Company; Washington (District of Columbia); On-site; USD 175,500 - 180,000; Full time

Benefits offered by this job

Competitive salary

World-class benefits

Job summary

McKinsey & Company is seeking a Data Scientist to blend analytics with semantic modeling and to transform raw data into actionable intelligence for products and business strategy.

You will design predictive models, run experiments, and surface insights while defining ontologies and knowledge graphs that connect enterprise data. Collaboration with engineers and domain experts is essential to scale data standards and model services.

Qualifications

• US Citizenship required.
• Defense experience and data handling at various classification levels.
• SECRET Security Clearance Required; TS+ clearance preferred.
• 3+ years of Data Scientist experience; Enterprise platform experience preferred.
• Deep knowledge of NLP, Transformers, LLMs, and Generative AI.
• Experience with OpenAI's GPT suite, including multimodal LLMs: GPT4V, GPT4o.
• Theoretical understanding of how LLMs can build classification models, re-rankers, and recommendation systems.
• Strong Python data modeling and pipeline development following best practices.
• Experience managing a Git repository with clean branching strategies.
• Experience with knowledge graphs.

Responsibilities

• Blend analytics with semantic modeling to transform raw data into actionable intelligence shaping products and business strategy.
• Design predictive models, run large-scale experiments, surface insights, and define ontologies and knowledge graphs that contextualize enterprise data.
• Collaborate with engineers and domain experts to establish data standards and operationalize models and graph services at scale.

Skills

NLP & LLMs

Python

OpenAI GPT suite

Knowledge graphs

Git

Classification models

Generative AI

Data modeling

Tools

Git

Job description

McKinsey & Company is seeking a Data Scientist to blend analytics with semantic modeling and to transform raw data into actionable intelligence for products and business strategy.

You will design predictive models, run experiments, and surface insights while defining ontologies and knowledge graphs that connect enterprise data. Collaboration with engineers and domain experts is essential to scale data standards and model services.