Alpha Capture Quantitative Researcher, Alternative Data
Overview
We are seeking a Quantitative Researcher to join a systematic equities team focused on developing and scaling alpha signals derived from alternative and non-market datasets. The role sits within a medium-horizon investment strategy with typical holding periods ranging from multi-day to multi-week and a strong emphasis on extracting predictive insights from investor behavior, positioning, flows, corporate events, and other differentiated data sources.
This is an opportunity to work on the entire research lifecycle, from data acquisition and signal discovery through portfolio implementation and performance attribution. The ideal candidate will have experience generating cross-sectional equity alpha from alternative datasets and a demonstrated track record of bringing research into production within a systematic investment environment.
Responsibilities

• Research, develop, and productionize alpha signals using alternative and non-traditional datasets across global equity markets.
• Identify predictive relationships within datasets such as investor positioning, fund flows, options activity, corporate events, transactional data, web data, consumer data, and other alternative information sources.
• Design medium-horizon investment signals targeting holding periods between one week and one month.
• Develop models that capture behavioral biases, crowding effects, positioning dislocations, sentiment shifts, and information inefficiencies.
• Conduct rigorous statistical analysis and validation of alpha signals, including robustness, scalability, capacity, and decay analysis.
• Partner closely with portfolio managers and quantitative developers to integrate signals into live portfolios.
• Enhance portfolio construction frameworks through signal weighting, risk management, optimization, and transaction cost modeling.
• Perform ongoing signal monitoring, attribution analysis, and performance diagnostics.
• Evaluate new data vendors and alternative datasets to develop differentiated sources of investment edge.

Preferred Research Areas
Candidates may have experience researching and deploying alpha signals derived from:

• Hedge fund positioning and ownership datasets
• Institutional and retail trading flow data
• Options flow, open interest, and derivatives positioning
• Corporate action and event-driven datasets
• Earnings-related behavioral signals
• Consumer and transaction data
• Web scraping, digital exhaust, and internet activity datasets
• Supply chain and geolocation data
• Expert network and proprietary dataset research
• Sentiment, news, and textual datasets
• Fund flow and capital allocation signals

Qualifications

• Advanced degree in a quantitative discipline such as Mathematics, Statistics, Physics, Computer Science, Engineering, Economics, or a related field.
• Demonstrated experience conducting alpha research within a systematic equities platform, hedge fund, proprietary trading firm, or asset manager.
• Strong understanding of cross-sectional equity modeling and factor research.
• Experience working with alternative datasets and transforming raw information into production-ready investment signals.
• Proficiency in Python and standard quantitative research libraries.
• Strong statistical and machine learning foundation with the ability to evaluate predictive efficacy across large datasets.
• Experience with portfolio construction, signal combination, and performance attribution is highly desirable.
• Ability to independently drive research from idea generation through live deployment.

Ideal Background
We are particularly interested in researchers who have successfully generated alpha from non-market datasets and investor behavior dynamics rather than traditional factor investing approaches. Relevant backgrounds may include systematic equity researchers from leading hedge funds, quantitative asset managers, or alternative data-focused investment platforms where they have:

• Built and deployed alpha signals from positioning, flow, event, or behavioral datasets.
• Demonstrated measurable contribution to live portfolio performance.
• Conducted end-to-end signal research, validation, implementation, and monitoring.
• Worked on medium-horizon strategies with holding periods measured in days to weeks rather than long term trading.
  
• Leveraged differentiated datasets to uncover crowding, information diffusion, sentiment, or positioning-driven opportunities.

What Success Looks Like
The successful candidate will deliver scalable and robust alpha signals that improve portfolio returns through differentiated alternative data research. They will bring creativity in sourcing and analyzing unique datasets while maintaining a disciplined, data-driven approach to signal validation, portfolio integration, and ongoing performance enhancement.
This role is particularly well suited for researchers from systematic investment organizations with a deep alternative data research culture.