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