JOB DESCRIPTION & OVERVIEW
Pluribus Labs is a systematic active equity manager with data science and machine learning at its core. We believe that unstructured, qualitative data represents a robust source of untapped insight. Founded by experienced investment and technology professionals and backed by Golden Gate Capital, Pluribus Labs seeks to unlock these insights by transforming qualitative data into quantitative insights.
We are seeking a quantitative researcher to join our investment team. Quantitative research at Pluribus Labs is a multidisciplinary effort at the intersection of large datasets, machine learning, and economics. We deal with large amounts of dirty, qualitative data and convert that data into economically defensible quantitative investment signals.
Above all, we place a premium on creativity in research. If you can form a non-obvious economic hypothesis, test the idea with data, and deliver superior investment signals, we want to hear from you.
This is an exciting role in a low-structure environment. The successful candidate will demonstrate an ability to conduct innovative research leading to insights for incorporation into our investment products.
Specific responsibilities include:
- Generate new research ideas that may produce investment insights that are uncorrelated with traditional factors
- Discover and ingest new, unstructured datasets: collection, parsing, and storage
- Contribute to in-house research frameworks: data processing, cleansing, modeling, visualization, etc.
- Build and maintain predictive statistical models from scratch, specifying key metrics for model validation
- Present and defend models, clearly articulating the economic rationale
- Generate new ideas for analysis using complicated datasets
- Build and maintain detailed predictive machine-learning models from scratch, utilizing disciplines such as natural language processing to mine unstructured data, and extract information from documents such as electronic filings, contracts, news, government statistics, etc.
- Backtest financial models to determine strength of signals
- Present insights to investment team; defend patterns and process in a Socratic environment
QUALIFICATIONS & REQUIREMENTS
The successful candidate will demonstrate the following skills and abilities:
- Expertise in Python; proficiency in distributed computing, graphs, SQL, NLP or some combination
- Substantial experience with unstructured data modeling
- Expertise in statistical libraries: Pandas, NumPy, Scikit-Learn, Statsmodels, NLTK, and Gensim
- Proficiency handling terabyte-sized datasets
- Practical experience using machine-learning solutions for complex data analytics and a nuanced understanding of appropriateness and constraints/shortcomings of various data-processing techniques
- Strong record of academic achievement or ability to demonstrate research proficiency. Undergraduate degree in a quantitative discipline (such as: Computer Science, Engineering/Financial Engineering, Data Sciences, Statistics, Mathematics, Physics or Life Sciences). Advanced degree preferred but not required.
- Familiarity with concepts pertaining to capital markets, equity investments, statistical techniques, and risk models
- Entrepreneurial mindset: ability to work independently and multitask in a low-structure environment to take an idea from inception to implementation
- Excellent written and verbal communication skills; willingness to defend your process and recommendations