Quantitative Researcher - Alternative Data for Equity Strategies
Role Overview
I am currently working with a successful 10 bil USD multi strategy hedge fund on the lookout for a highly analytical and technically skilled Quantitative Researcher to join their equity alpha esearch team with a focus on alternative data. This role is ideal for someone passionate about uncovering alpha through non-traditional datasets and building scalable research pipelines that support systematic and discretionary equity strategies.
Key Responsibilities
Alpha Signal Development
- Design and validate predictive signals using alternative datasets such as credit card transactions, web traffic, app usage, email receipts, geolocation, job postings, and proprietary scrapes
- Apply statistical and machine learning models (e.g., NLP, time-series forecasting, ensemble methods) to extract insights from structured and unstructured data
Data Engineering & Pipeline Construction
- Build robust ETL pipelines for ingesting, cleaning, and transforming raw data into research-ready formats
- Collaborate with data vendors and internal engineering teams to onboard new datasets and maintain data quality
Backtesting & Performance Evaluation
- Conduct rigorous backtesting of signals across global equity universes
- Evaluate signal efficacy using metrics such as IC, IR, Sharpe ratio, and turnover
Cross-Functional Collaboration
- Work closely with PMs, analysts, and data strategists to translate data insights into investment hypotheses
- Support live strategy monitoring and contribute to portfolio construction discussions
Qualifications
Education
- Master's or PhD in Computer Science, Statistics, Financial Engineering, Applied Mathematics, or related fields
Experience
- 2+ years of experience in quantitative research or data science within equities
- Proven track record of working with alternative data in an investment context
- Experience with both systematic and discretionary investment teams is a plus
Technical Skills
- Strong programming skills in Python and SQL; familiarity with Spark, Airflow, or cloud platforms (AWS/GCP) is a plus
- Experience with NLP, time-series modeling, and supervised learning techniques
- Familiarity with financial databases (e.g., Bloomberg, FactSet, CapIQ) and alternative data vendors
Soft Skills
- Strong communication and presentation skills
- Ability to work independently and proactively in a fast-paced environment
- Entrepreneurial mindset with a passion for data-driven investing
Bonus Points
- Prior experience at hedge funds or asset managers with alternative data teams
- Experience building dashboards or research tools for investment teams
- Contributions to open-source data science or finance projects
- Publications or presentations in quantitative finance or data science conferences
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