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# TMT Data Scientist

- **Company:** Balyasny Asset Management L.P.
- **Location:** New York, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Microsoft Excel, Artificial Intelligence, Airflow, Amazon Web Services, Data Analysis, Microsoft Azure, Cloud Computing, Data Visualization, Python (Programming Language), Machine Learning, Standard Sql, Tableau (Software), Web Application Frameworks, Google Cloud, Large Language Models, Snowflake, Prompt Engineering, Apache Spark, Jupyter, Matplotlib, Information Technology, Low Latency, Data Analytics, Plotly, Streamlit Framework, Automation Anywhere, Docker, Jenkins, Databricks
- **Published:** August 28, 2026
- **Apply:** https://www.dice.com/job-detail/ac34251b-37ee-4719-b0c2-c8b2823253f5

## About the Role

* Alpha-First Mindset: A passion for delivering value to investment teams by questioning the consensus and looking for informational advantages in data
 * Technical Mastery: Solid command of statistics, analytics, and machine learning, with a proven track record of informing investment or business decisions. Familarity with panelization, demographic analyses, or other relevant areas of data science
 * Sector Expertise: knowledge of the TMT investing space and direct experience working with key sector datasets
 * AI applications: experience or familiarity with LLMs and prompt engineering, coding agents, and multi-step workflow build-outs
 * Proven Experience: 7+ years in data science, data analytics, or a related field
 * Educational Background: BS or MS in Mathematics, Finance/Economics, Computer Science, Statistics, Engineering, or Technology
 * Crisp Presentation & Communication: Ability to distill complex technical concepts to non-technical audiences. Motivation to understand and integrate both up and downstream processes.
 * Coding: Python and SQL
 * Tech Stack Familiarity : Experience with any of the following is a plus: data analytics (Databricks/Spark, Snowflake, Jupyter), visualization tools (matplotlib, plotly, Tableau), web application frameworks (Dash, streamlit), cloud infrastructure (AWS/Google Cloud Platform/Azure, Apache Airflow, Jenkins, Docker), and Excel

Join BAM's Equities Data Science & AI team and help shape the future of data-driven investing. If you're passionate about leveraging data to solve complex problems and drive investment performance, we want to meet you.

## Description

Balyasny Asset Management (BAM) operates at the intersection of finance and technology. Our teams bring together portfolio managers, financial analysts, quantitative researchers, and software engineers who work together to identify investment opportunities and generate lasting returns for our investors. BAM's Equities Data Science & AI (DSAI) team is a group of data scientists, engineers, and researchers who deliver data- and AI-driven insights to BAM's portfolio managers. We are seeking an exceptional Data Scientist to join our team focused on the large and high-growth TMT sector. In this role, you will collaborate directly with portfolio managers to design and deliver data-driven solutions that address the most critical TMT investment debates and build custom AI workflows that enhance the investment process.

What You'll Do:

 * Partner with Portfolio Managers: Identify and frame key investment debates in The TMT sector, and design and conduct data-driven research to address them
 * Enable with AI: Partner with portfolio managers to apply BAM's robust AI platform to custom agentic solutions that enhance their investment process
 * Forecast the Future: Use advanced techniques (LLMs, graph analytics, Bayesian frameworks) to create predictions with high accuracy and low latency
 * Build & Scale Data Products: Lead the development of data products and scale solutions that empower your sector's investment teams and drive alpha generation
 * Shape Sector Strategy: Contribute to the development and execution of sector-level data strategies, driving engagement and adoption of the team's product
 * Innovate with Investment Staff: Brainstorm creative applications of data in the investment process, pushing the boundaries of what's possible


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