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- Tasks: Join our Private Equity Data Science team to innovate with AI and quantitative models.
- Company: Goldman Sachs, a leading global investment banking and management firm.
- Benefits: Competitive salary, health insurance, generous vacation, and wellness programs.
- Why this job: Make a real impact in finance using cutting-edge data science techniques.
- Qualifications: PhD in a quantitative field and strong programming skills required.
- Other info: Dynamic work environment with excellent growth opportunities and commitment to diversity.
The predicted salary is between 36000 - 60000 £ per year.
Join our Private Equity Data Science team and contribute to DSML and AI initiatives across the full lifecycle of the investment process. The Data Scientist will be responsible for the design, development, and implementation of quantitative and data-driven models to drive innovation and productivity for origination, due diligence, and investment performance. The data science team sits alongside the Goldman Sachs Private Equity Deal Teams and works closely with the Goldman Sachs Value Accelerator and portfolio company management teams.
Key Responsibilities
- Leverage sophisticated statistical, mathematical, and programming skills to analyse complex datasets, support the investment processes, and drive quantifiable commercial value.
- Partner with Deal Teams to define and deliver data-driven origination initiatives.
- Deliver quantitative analyses through investment due diligence; translating complex data into comprehensive analyses assessing potential risk and opportunities in tight timelines.
- Partner strategically with portfolio company management teams to drive data and AI initiatives for value creation.
- Partner with GS Engineering to lead development and implementation of data-centric tools, enhancing our investment processes and supporting our deal and fundraising teams.
- Stay up-to-date with the latest developments in AI, ML, and related fields to continuously improve the division's AI capabilities.
Qualifications, experience, and attributes
- PhD or equivalent in a quantitative field such as Mathematics, Computer Science, Physics or in a related field.
- 2+ years of relevant experience applying quantitative methods to commercial problems.
- Strong programming skills (Python, SQL) and experience using the basic data science libraries (e.g. pandas, scikit-learn).
- High-level of proficiency in mathematics, statistics, and data science theory.
- Proven experience implementing sophisticated data science techniques, handling large datasets, translating data into actionable business insights.
- Commercial experience with a strong track record of quantitative problem solving and realised commercial impact.
- Excellent written and verbal communication and collaboration skills with a strong growth mindset.
Asset & Wealth Management - Quantitative Engineering - Associate - London London · United Kingd[...] employer: Goldman Sachs Bank AG
Contact Detail:
Goldman Sachs Bank AG Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Asset & Wealth Management - Quantitative Engineering - Associate - London London · United Kingd[...]
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend events, and connect on LinkedIn. You never know who might have the inside scoop on job openings or can put in a good word for you.
✨Tip Number 2
Prepare for interviews by practising common questions and showcasing your quantitative skills. Use real-world examples from your experience to demonstrate how you've tackled complex problems and delivered results.
✨Tip Number 3
Stay updated on the latest trends in AI and data science. Being knowledgeable about current developments will not only impress interviewers but also show that you're passionate about the field.
✨Tip Number 4
Don't forget to apply through our website! It’s the best way to ensure your application gets noticed. Plus, it shows you're genuinely interested in joining our team at Goldman Sachs.
We think you need these skills to ace Asset & Wealth Management - Quantitative Engineering - Associate - London London · United Kingd[...]
Some tips for your application 🫡
Tailor Your Application: Make sure to customise your CV and cover letter for the role. Highlight your relevant experience in quantitative methods and data science, and show how your skills align with what we’re looking for at StudySmarter.
Showcase Your Skills: Don’t just list your programming skills; demonstrate them! Include specific examples of projects where you’ve used Python or SQL to solve complex problems. We love seeing how you’ve applied your knowledge in real-world scenarios.
Be Clear and Concise: When writing your application, keep it straightforward. Use clear language and avoid jargon unless necessary. We appreciate a well-structured application that’s easy to read and gets straight to the point.
Apply Through Our Website: We encourage you to submit your application through our website. It’s the best way to ensure your application gets seen by the right people. Plus, you’ll find all the info you need about the role and our company culture there!
How to prepare for a job interview at Goldman Sachs Bank AG
✨Know Your Quantitative Stuff
Make sure you brush up on your quantitative skills, especially in mathematics and statistics. Be ready to discuss how you've applied these skills in real-world scenarios, particularly in investment processes or data-driven models.
✨Show Off Your Programming Skills
Since strong programming skills in Python and SQL are crucial for this role, prepare to demonstrate your proficiency. You might be asked to solve a coding problem or explain how you've used libraries like pandas or scikit-learn in past projects.
✨Understand the Business Context
It's not just about the numbers! Be prepared to discuss how your analyses have translated into actionable business insights. Think of examples where your work has had a quantifiable impact on commercial decisions or risk assessments.
✨Stay Current with AI Trends
The role involves leveraging AI and machine learning, so make sure you're up-to-date with the latest developments in these fields. Be ready to share your thoughts on how these technologies can enhance investment processes and drive value creation.
