Brooklyn Investment Group is an SEC-registered Investment Adviser that leverages the technology of its parent company, Brooklyn Artificial Intelligence Research, a pioneer in building A.I. that understands human preferences through natural language. This technology is combined with institutional-grade portfolio optimization and automated tax-loss harvesting to power personalized portfolios for our clients. Over the past five years, Brooklyn Artificial Intelligence Research has developed one of the most powerful A.I. engines for customizing investment portfolios and more than $5 billion has been traded in portfolios created by its A.I. to date. Our growing team of 15 quant investors, machine learning researchers, and software engineers comes from top universities and financial institutions such as Goldman Sachs and Bridgewater Associates.
As a quantitative research analyst, you will work on large datasets to derive insights and improve our investment portfolios. Rigorous data work involves parsing and merging datasets, generating sample statistics to monitor data quality, and working with our research and production data infrastructure on a SQL server. Quantitative analysis involves fitting models to explain market behavior, building and testing quant signals to predict future stock returns, running historical backtests to understand and optimize portfolio performance, and monitoring portfolio returns and risk exposures. All work will be in close collaboration with senior researchers, giving you the chance to learn and grow as a researcher.
Strong coding skills in Python, including pandas and numpy, are required. Strong skills in math and statistics are also required; experience with machine learning is highly desirable. While having a keen interest in investing is crucial, a background in finance is not a requirement. However, you must be passionate to learn.
In general, we try to foster a supportive environment that values exploration, innovation, open communication, collaboration, and efficiency across various areas of the firm.
Qualifications

• Masters degree from a leading institution in a quantitative discipline, such as math, natural sciences, engineering, computer science, and economics
• Two or more years of professional work experience in the finance domain
• Quantitative Skills: We are looking for someone who has a deep understanding of advanced statistical and quantitative methods. Hands-on experience is necessary in conceptualizing real-world problems, implementing models and evaluating results.
• Continuous Drive for Improvement: Are you naturally inclined to strive for excellence and growth, even after achieving significant accomplishments?
• Creative Problem Solving and Probabilistic Thinking: Do you find joy in rapidly acquiring new knowledge, integrating ideas from diverse domains, and employing a data-driven and probabilistic approach to test and implement innovative concepts?
• First-Principles Structured Thinking: We are seeking individuals who possess a strong aptitude for first-principles structured thinking. This involves the ability to break down complex problems into fundamental components, analyze them independently, and arrive at novel solutions based on fundamental principles rather than relying solely on existing knowledge or assumptions.
• Collaborative Mindset : We seek individuals who understand the power of teamwork, valuing the collective strength that surpasses individual contributions.
• Proficiency in Python: We are looking for candidates who demonstrate a high level of skill in writing Python code, especially libraries in the data science 'stack' such as scikit, pandas and numpy. This includes adhering to best practices in software development, such as writing clean, modular, and efficient code.
• Quantitative Equities Experience (Preferred But Not Required ): While prior experience in quantitative equity strategies is preferred, it is not mandatory for this position. We value candidates who bring diverse backgrounds and perspectives to our team. However, if you have previous experience in quantitative equity research, portfolio management, or related fields, it would be considered a valuable asset. Familiarity with concepts like factor-based risk models, generation of alpha signals, and performance attribution will allow you to start contributing sooner to our quantitative investment strategies.

If you're interested, please send your CV, GitHub profile, and a short paragraph on why you'd be an excellent addition to the company to hiring@bkln.com.
Anticipated range of $125,000-$225,000 for base salary, depending on the amount of compensation desired in equity options, and on experience level.