Quantitative Investment Solutions Analyst

Analytics & Risk Management - Analytics


The Quantitative Investment Solutions (QIS) Analyst supports the firm’s quantitative alpha generation by identifying, researching, developing and testing quantitative investment strategies and predictive analytic models for investment decision support.

The QIS Analyst uses traditional statistical and machine learning data science methods applied to time series and alternative data sources to identify and manage algorithms that can be used to: identify investment signals; predict movements in: macro systematic factors; sectors or issuer performance; and/or inform portfolio positioning and risk; in the Fixed Income markets.

The QIS Analyst collaborates on the identification of quantitative alpha generating ideas and works more independently on: sourcing, validation and scrubbing of source data; development of quantitative algorithms using statistical programming languages; testing, monitoring and tuning developed methods; and documenting and supporting implemented strategies.

Producing these quantitative analyses requires a strong quantitative background including a deep knowledge of statistics and working experience with machine learning, statistical programming skills, and ideally a keen understanding of the fixed income markets, market theory, and portfolio construction.

Along with the specified responsibilities and competencies, we are looking for a self-motivated and detail oriented individual who possesses a positive, “can do” attitude, works effectively as a team member in a collaborative environment, and has a keen interest in a quantitative career in the financial markets.


  • Participate in team efforts to identify potential alpha generating investment theses
  • Research, source, analyze, cleanse and manipulate new and existing sources of economic, investment, and other market structured and unstructured data
  • Utilize machine learning and other statistical techniques to develop (and enhance) algorithmic investment strategies by writing code using statistical programming languages (SAS, Python, R)
  • Validate, interpret and communicate quant model results with an appropriate understanding of statistical validation techniques and model risk
  • Work with other organizations within the firm to support robust (and rapid) production implementation of models
  • Monitor ongoing model performance and enhance or tune models as necessary and extend techniques for identifying and addressing data issues


  • Expertise and 2-4 years of commercial experience in the application of data mining and machine learning techniques, preferably to financial markets; additional prior experience with statistical analysis in finance is a plus
  • Strong statistical programming experience preferably in SAS, R and/or Python; strong SQL or NoSQL experience a plus
  • Knowledgeable in leading edge technologies and commercial sources of “big” data; familiarity with natural language a plus
  • Expertise in Fixed Income instruments, portfolio strategies and benchmarks is preferred
  • Creative analytical skills; capable of original thinking to synthesize potential investment theses and models
  • Excellent verbal and written communication skills; able to effectively communicate complex algorithmic concepts in investment terms to non-quants
  • Team player; possesses the ability to work collaboratively with investment professionals and other quants
  • Self-motivated, detail-oriented and proactive; able to take ownership of projects from start to finish and work in a fast-paced environment


Academic Qualifications (minimum requirements):

  • Advanced degree in data science, statistics, mathematics or similar quantitative discipline (MS or PhD)
  • Minimum 2-4 years of experience working in a relevant capacity, preferably for a Financial firm

How to Apply

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