Goldman Sachs Alternatives A career with Goldman Sachs Asset Management is an opportunity to help clients across the globe realize their potential, while you discover your own. As part of one of the world's leading asset managers, you can expect to participate in exciting investment opportunities while collaborating with talented colleagues from all asset classes and regions, and building meaningful relationships with your clients. Working in a culture that values integrity and transparency, you will be part of a diverse team that is passionate about our craft, our clients, and building sustainable success.
The role: Join our Real Estate Research Analytics team and contribute to analytics and DSML initiatives across the full lifecycle of the investment process. This role will be responsible for the design, development, and implementation of quantitative analytics and machine learning models to drive innovation for investment strategy, origination, due diligence, and asset management. The Research team works directly with Goldman Sachs Real Estate Fund Leaders, Sector Heads, Deal Teams, and others.
Key Responsibilities: Leverage sophisticated statistical, mathematical, and programming skills to analyse traditional and alternative datasets to support the investment processes. Partner with the Real Estate business to identify and evaluate new datasets and external analytics solutions to empower novel analysis. Design, implement, and continually refine analytics and machine learning models that inform key strategic and investment-level decisions. Contribute to data and analytics infrastructure that supports the team's research and analysis workstreams. Stay up-to-date with the latest developments in analytics, DSML, and related fields to continually improve the team and division's capabilities. Qualifications, experience, and attributes: MS or equivalent in a quantitative field such as Data Analytics, Mathematics, Computer Science, Physics or related field. 2+ years of relevant experience applying analytics to commercial problems, translating data into actionable business insights. High-level of proficiency in analytics, mathematics, statistics, and data science theory. Strong programming skills (Python, SQL) and experience using basic quantitative and data science libraries (e.g., pandas, scipy.stats, scikit-learn). Excellent written and verbal communication and collaboration skills with a strong growth mindset. Preferred experience with planning and building ETL pipelines using common frameworks (e.g., Apache Airflow, AWS Glue). Preferred experience with common data visualization tools (e.g., Power BI, Tableau). Preferred to have basic familiarity with GenAI approaches, frameworks, and infrastructure.
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