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.

Why Join the Arootah Network

• Alt-industry-only engagements with hedge funds, private equity firms, and family offices

• Project-based work that fits around existing commitments

• Client origination and engagement management handled by Arootah; advisors focus on delivery

About Arootah

Who We’re Looking For

Core Areas of Expertise

• 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.
• 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.

What You Bring

• 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.