Balyasny Asset Management (BAM) is seeking to solve the problem of identifying investment opportunities and generating lasting returns for investors by leveraging data science and AI to inform investment decisions.
Requirements
- Solid command of statistics, analytics, and machine learning, with a proven track record of informing investment or business decisions.
- Experience or familiarity with LLMs and prompt engineering.
- Python and SQL
- 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/GCP/Azure, Apache Airflow, Jenkins, Docker), and Excel.
Responsibilities
- Partner with Portfolio Managers: Identify and frame key investment debates in the Consumer & TMT sectors, and design and conduct data-driven research to address them.
- Forecast the Future: Use advances techniques (LLMs, graph analytics, machine learning, Bayesian frameworks) to create predictions which have high accuracy and low latency.
- Leverage AI: Use the latest in LLMs and BAM’s AI infrastructure to extract insights and deliver Agentic solutions for our Portfolio Management teams.
- Build & Scale Data Products: Lead the development of data products and scale solutions that empower 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 products.
- Innovate with Investment Staff: Brainstorm creative applications of data in the investment process, pushing the boundaries of what’s possible.
Other
- Alpha-First Mindset: A passion for delivering value to investment teams by questioning the consensus and looking for informational advantages in data.
- Continuous Pursuit of Excellence: The ability to deliver results and an internal drive to constantly iterate and improve.
- Educational Background: BS or MS in Mathematics, Finance/Economics, Computer Science, Information Management, Statistics, Engineering, or Technology.
- Crisp Presentation & Communication: Ability to distill complex technical concepts to non-technical audiences.
- Proven Experience: 4+ years in data science, data analytics, or a related field.
