Fractional Data Scientist (Advisor Network)

Opportunity Overview

Arootah engages a curated network of senior operators to support clients across the alternative investment industry, including hedge funds, private equity firms, and family offices. Advisors are deployed on project-based engagements when client needs match their expertise. Joining the network does not guarantee placement; it provides access to opportunities as they arise.

• 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.

Important Note

This opportunity is to join Arootah’s advisor network and is not tied to a specific active engagement. Advisors are engaged on an as-needed basis, based on client demand and fit.

• 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.
• 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.