Alpha Capture Quantitative Researcher, Alternative Data
- 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.
- 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
- 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.
- 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.
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