Role Overview

The Data Science & AI organization is building an elite AI enablement team to accelerate adoption of BAM’s in-house generative AI and data science products across investment strategies. In this role, you will embed directly with portfolio managers and analysts, training users, evangelizing new features, and implementing agentic solutions that measurably improve traders’ decision speed and research processes. You will act as the “last mile” between our platform and the investment desk – driving adoption of in-house tools, rapidly prototyping agentic workflows, and funneling feedback back to BAM’s Data Science and AI teams.

Key Responsibilities

Drive Adoption: Embed with trading desks to maximize the value derived from BAM’s in-house generative AI and data science tools
Partner Closely: Translate PM and analyst requirements into concrete technical solutions
Train & Enable: Deliver demos, host office hours, and create cheat-sheets to simplify every PM & analysts’ journey with our solutions
Streamline Processes: Continuously evaluate the workflows of our investment teams and use LLMs and data science techniques to improve efficiency and investment decision making
Relay Feedback: Capture on-desk insights and relay them to the appropriate Data Science and AI teams to sharpen and extend existing products.
Maintain Edge: Track emerging LLM techniques and machine learning methods, pilot promising ideas, and feed proven wins back into the platform.

Qualifications

Education:

Master’s degree in Data Science, Computer Science, Statistics, Mathematics, or equivalent work experience. Advanced degrees are preferred.
Academic training in computer science, statistics and data science techniques (including machine learning methods) is required
Preference for candidates with academic training in generative AI techniques

Experience:

At least 3 years of experience in a customer facing role, scoping, developing and deploying LLM-based and data science solutions Candidates must have demonstrable experience building LLM-based applications
Preference for candidates with prior buy-side or equity research experience – hands-on company analysis and direct collaboration with portfolio managers

Technical Skills

Highly proficient in the use of Python for data manipulation, statistical modeling and machine learning, along with strong SQL skills for database querying.
Experience integrating LLMs and AI models into production workflows is a must
Familiarity with OpenAI APIs and LLM frameworks such as LangChain and LlamaIndex.
Familiarity with AWS or other cloud platforms, Git for version control, and Airflow (or similar orchestration tools) is a strong plus.

Soft Skills

Strong problem-solving and analytical skills
Excellent communication and interpersonal skills
Ability to work independently and as part of a team
Demonstrated ability to manage multiple projects and meet deadlines

Balyasny Asset Management L.P.

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