Fractional Data Scientist (Advisor Network)
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AI Summary
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
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Alt-industry-only engagements with hedge funds, private equity firms, and family offices
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Project-based work that fits around existing commitments
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Client origination and engagement management handled by Arootah; advisors focus on delivery
Arootah is a talent solutions firm purpose-built for the alternative investment industry. We advise and we execute, covering the full talent spectrum from entry to exit across five services: Talent Acquisition, Fractional Leadership, Talent Development, Retention & Compensation, and Transition.
Most talent firms solve one problem. Because we cover the full spectrum, we can help clients identify and solve the right one. We start with diagnosis, then deliver.
Our team has run alternative investment firms at the highest levels as COOs, CFOs, and CCOs. That operator experience, combined with 15+ years of alternatives-specific recruiting and a network of 700+ vetted advisors across 18 functional disciplines, is what makes the advice credible and the execution reliable. We also draw on a network of 600+ coaches, deploying those with specific experience guiding senior executives in the alternatives industry.
One industry. One talent partner. Entry to exit.
Learn more: https://arootah.com/
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Best practice reviews.
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Developing realistic and effective action plans.
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Breaking apart goals into actionable steps.
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Advising on vendor selection and oversight.
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Creating and implementing policies, procedures, and control measures.
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Evaluating each client’s advancement toward goal actualization through key performance indicators (KPIs) and scoring matrices.
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Special projects or other areas of need.
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Scope, build, and lead the investment data science and analysis practice for the investment team of a Hedge Fund or Family Office.
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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.
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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.
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Build portfolio company dashboards to track all relevant KPIs and value creation metrics for existing portfolio investments.
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Track and benchmark existing companies or investment alternatives in the pipeline by building a platform to help analyze datasets across all target investments.
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Maintain and update all internal datasets for comparable valuation metrics across all investment team funds and strategies.
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Work closely with the Technology and IT team to develop a best-in-class technology stack to enable the firm's data science practice.
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Investigate data quality issues affecting investment analytics and risk metrics; coordinate resolution with other groups as required.
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Promptly respond to investment data requests from key customers, including Traders, Portfolio Managers, and investment personnel.
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Continually investigate new tools and techniques, as well as emerging standards to support investment data controls and operational readiness for investment mandates.
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A Bachelor’s Degree in Computer Science, Data Science, Engineering, Information Systems, or a related field.
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Master’s Degree in a quantitative field (e.g. Computer Science, Mathematics, Statistics, or other related field) is a plus.
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6+ years of proven experience as a Data Scientist at a Hedge Fund, Investment Management firm or Family Office.
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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.
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Hands-on expertise and knowledge of emerging data science techniques, technologies, and potential business applications for Machine Learning/Artificial Intelligence.
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Demonstrated understanding of financial concepts, different asset classes, investment vehicles, security types, and application of data science in financial domain.
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Comfort with high volumes of unstructured and structured data, with the ability to leverage multiple datasets to draw both granular and big picture conclusions
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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.
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