TMT Data Scientist

About the role

Balyasny Asset Management (BAM) operates at the intersection of finance and technology. Our 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.

Responsibilities

• 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.

Requirements

• 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. Familiarity 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 upstream and downstream processes.
• Coding: Proficiency in Python and SQL.

Skills

• Data analytics (Databricks/Spark, Snowflake, Jupyter)
• Visualization tools (matplotlib, plotly, Tableau)
• Web application frameworks (Dash, streamlit)
• Cloud infrastructure (AWS/GCP/Azure, Apache Airflow, Jenkins, Docker)
• Excel