Data Scientist, Consultant
June 3
Description
• Scope, build, and lead the investment data science and analysis practice for the investment team of a Hedge Fund or Family Office. • Perform exploratory analyses as well as clustering and modeling of large structured and unstructured datasets in order to deploy production-ready models in the domains of lead scoring, look-a-like, retention and more, including post-deployment monitoring. • Partner closely with a firm’s investment team to turn datasets into actionable insights by attending weekly pipeline meetings and working 1:1 with investment team leads to support the sourcing, selection and monitoring of all investment targets. • Build portfolio company dashboards to track all relevant KPIs and value creation metrics for existing portfolio investments. • Track and benchmark existing companies or investment alternatives in the pipeline by building a platform to help analyze datasets across all target investments. • Maintain and update all internal datasets for comparable valuation metrics across all investment team funds and strategies. • Work closely with the Technology and IT team to develop a best-in-class technology stack to enable the firm's data science practice. • Investigate data quality issues affecting investment analytics and risk metrics; coordinate resolution with other groups as required. • Promptly respond to investment data requests from key customers, including Traders, Portfolio Managers, and investment personnel. • Continually investigate new tools and techniques, as well as emerging standards to support investment data controls and operational readiness for investment mandates.
Requirements
• A Bachelor’s Degree in Computer Science, Data Science, Engineering, Information Systems, or a related field. • Master’s Degree in a quantitative field (e.g. Computer Science, Mathematics, Statistics, or other related field) is a plus. • 6+ years of proven experience as a Data Scientist at a Hedge Fund, Investment Management firm or Family Office. • Proficiency in Python, R, and SQL as well as data visualization and dashboarding tools (e.g. Tableau, Looker, QlikView) and Big Data technologies such as Apache Spark and cloud computing platforms. • Hands-on expertise and knowledge of emerging data science techniques, technologies, and potential business applications for Machine Learning/Artificial Intelligence. • Demonstrated understanding of financial concepts, different asset classes, investment vehicles, security types, and application of data science in financial domain. • Comfort with high volumes of unstructured and structured data, with the ability to leverage multiple datasets to draw both granular and big picture conclusions. • Strong written and spoken communications skills; ability to communicate ideas clearly and confidently, articulate issues and recommend solutions.
Benefits
• Best practice reviews. • Developing realistic and effective action plans. • Breaking apart goals into actionable steps. • Advising on vendor selection and oversight. • Creating and implementing policies, procedures, and control measures. • Evaluating each client’s advancement toward goal actualization through key performance indicators (KPIs) and scoring matrices. • Special projects or other areas of need.
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