Sr. Data Scientist, Private Investments

Viking Global Investors LP • New York, NY • 5h ago

Sr. Data Scientist, Private Investments w/ Viking Global Investors (New York, NY). Identify investable opportunities for private equity and structured credit using alternative data sources. Develop custom analytics to facilitate the identification of potential acquisition targets through the use of alternative datasets. For example, use large datasets that contain software reviews to identify mid-market software companies that are growing fast and gaining market shares within their category. Use sentiment analysis to assess customers perception of their software products. Leverage vast repository of alternative data to triangulate private investment insights across multiple datasets. Evaluate business quality and growth potential of investable opportunities for private equity and structured credit using alternative data sources and company data. Develop repeatable analyses aimed at evaluating current and potential future value of private companies. Leverage a combination of alternative datasets and customer data provided by the investment targets. For example, quantify the overall size of the market segment in which the target operates and their share of the total. Use Tableau to visualize customers purchasing behavior and easily distinguish between core customers who make repeated purchases and the occasional customers who purchase infrequently. Identify and evaluate new data sources. Read industry publications (e.g. specialized reports published by Goldman Sachs and other investment banks) to identify new datasets that could potentially improve ability to analyze private investments. Engage with the providers of such datasets to understand how the data is collected and if it has any biases (e.g. whether there is a higher proportion of high-earners in the data than in the US population). Evaluate whether the forecasts based on newly identified datasets are more accurate than those based on more established datasets that were used the previous month. Streamline the data life cycle, operating model, and process for private investments. Evaluate every few months whether the datasets that were used in the past continue to be as useful to forecast various economic and financial trends. Integrate Artificial Intelligence in all phases of the private deal lifecycle. Develop proto@types to automate and scale the collection of data on private companies from public websites, and to streamline their analysis. Leverage AI to scan vast amounts of web pages and extract ready-to-analyze data points that would not otherwise be readily available for purchase from Alternative Data vendors. Collaborate with investment staff to integrate these proprietary AI tools in their investment process. For example, develop automated workflows powered by Large Language Models and other AI technologies to identify all the companies that operate in a specific segment, visit their websites to extract key information about their product, their store footprint, and other aspects of their operations, and consolidate the information in a searchable database that can be easily analyzed. Create centralized and automated analyses and processes. Use coding languages such as Python and SQL to automate certain calculations. Review calculations every few months to ensure that the quality of the forecasts continues to be high so that our Investment Team can continue to rely on them to make investment decisions. Requires at least 2 years of professional experience in a data science role at a Private Equity Fund working on datasets relevant for private companies. Must have 2 years of experience: applying statistics, machine learning and data science best practices to financial markets; building automated workflows powered by Artificial Intelligence to streamline investment research in private markets; embedding data science techniques and best practices in the private investment process; and utilizing Python and statistical libraries, as well as SQL, BI softwa

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