Quantitative Researcher

Asset Manager is seeking an experienced Fundamental Equities Quantitative Analyst to join their Investment Management team. This role bridges the gap between traditional fundamental equity analysis and quantitative, data-driven investing. You will integrate high-quality fundamental insights (financial modeling, industry expertise) with advanced quantitative techniques (alternative data analysis, factor modeling, machine learning) to generate superior alpha and enhance portfolio construction. The ideal candidate possesses deep financial acumen and strong programming skills to research, test, and implement fundamental and systematic equity strategies.

• Fundamental Quant Research:
  Conduct in-depth fundamental research and integrate with rigorous quantitative techniques to identify investment opportunities and mispricings.
• Collaboration:
  
  Work closely with fundamental analysts and Portfolio Managers to integrate quantitative insights into investment strategies. Translate complex, discretionary investment theses into testable, systematic rules and scalable algorithmic signals.
• Portfolio Construction:
  Partner with portfolio managers to improve portfolio construction, optimization, and risk management using advanced tools and models including BARRA and Axioma.
• Factor Construction:
  Develop stock selection models and proprietary risk factors to enhance the fundamental investment process.
• Research Infrastructure:
  Develop an efficient research workflow for rapid testing of ideas. Contribute to proprietary libraries for data ingestion, signal processing, and performance attribution, ensuring rapid time-to-insight.
• Alternative Data Integration:
  Engineer high-dimensional signals by fusing traditional fundamental datasets (IBES, Compustat) with unstructured alternative data, utilizing NLP/LLMs to parse earnings call transcripts, 10-K/10-Q filings, and management sentiment.

• Fundamental Intuition & Systematic Rigor:
  Deep understanding of corporate finance, accounting principles, and equity valuation, paired with the statistical rigor required to build production-grade quantitative models.
• Advanced Signal Engineering:
  Proven ability to clean, map, and extract predictive features from massive, cross-sectional equity datasets, handling point-in-time data complexities and survivorship bias flawlessly.
• NLP, ML & Unstructured Data Mastery:
  Expertise in applying modern NLP techniques and LLM architectures to extract nuanced sentiment and factual data from complex financial documents. Mastery of traditional machine learning techniques.
• Factor Modeling:
  Deep knowledge of commercial risk models (Barra, Axioma) and proven ability to enhance and extend such models.
• Communication:
  Ability to fluently speak the language of both deep quantitative methods and traditional fundamental stock picking, fostering trust and collaboration with fundamental analysts and portfolio managers.

• Required Education
  • MSc or PhD from a top-tier institution in a highly quantitative field (Financial Engineering, Statistics, Applied Mathematics, Computer Science, or Physics). CFA designation is a strong advantage.
• Experience
  • 5 to 10 years of direct experience as a Quantitative Researcher focusing on fundamental equities, quanta mental strategies, or statistical arbitrage at a top-tier asset manager or hedge fund.

• Programming & Data Science:
  Expert-level Python (Pandas, Num Py, Sci Py) and advanced SQL. Experience with C++ or Rust for performance-critical components is a plus.
• Financial Datasets:
  Extensive hands-on experience with point-in-time fundamental and pricing databases (e.g., Compustat, Worldscope, IBES, CRSP, Fact Set, Bloomberg).
• Machine Learning & NLP:
  Proficiency in ML libraries (scikit-learn, PyTorch) and modern NLP frameworks (Hugging Face, Lang Chain) for processing financial text.
• Quantitative Finance:
  Deep understanding of cross-sectional equity modeling, factor neutralization, and convex optimization techniques.
• Software Engineering:
  Strong adherence to modern software engineering practices, including Git version control, CI/CD pipelines, and containerization (Docker).
• Big Data Ecosystems:
  Familiarity with distributed computing and modern data warehousing (e.g., Snowflake, Databricks, Apache Spark) to handle alternative data scale.