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- Tasks: Join our Private Equity Data Science team to innovate with AI and data-driven models.
- Company: Goldman Sachs, a leading global investment banking and management firm.
- Benefits: Diverse opportunities for growth, wellness programs, and a supportive work culture.
- Why this job: Make a real impact in finance using cutting-edge data science techniques.
- Qualifications: PhD or equivalent in a quantitative field and strong programming skills required.
- Other info: Collaborative environment with excellent career advancement opportunities.
The predicted salary is between 36000 - 60000 £ per year.
The role: 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 - Private Equity Data Science - Associate - London employer: WeAreTechWomen
Contact Detail:
WeAreTechWomen Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Asset & Wealth Management - Private Equity Data Science - Associate - London
✨Network Like a Pro
Get out there and connect with folks in the industry! Attend meetups, webinars, or even just grab a coffee with someone from the Private Equity Data Science scene. We all know that sometimes it’s not just what you know, but who you know that can land you that dream job.
✨Show Off Your Skills
Don’t just talk about your programming skills; show them! Create a portfolio of projects using Python and SQL that demonstrate your ability to analyse complex datasets. We love seeing real-world applications of your skills, so make sure to highlight those in conversations.
✨Stay Current with Trends
Keep yourself updated on the latest in AI and ML. Follow relevant blogs, join online forums, or take a quick course. We want to see that you’re passionate about continuous learning and ready to bring fresh ideas to the table!
✨Apply Through Our Website
When you find a role that excites you, don’t hesitate! Apply through our website to ensure your application gets the attention it deserves. We’re eager to see how you can contribute to our team and help drive innovation in the investment process.
We think you need these skills to ace Asset & Wealth Management - Private Equity Data Science - Associate - London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to the role. Highlight your experience in data science, especially any work with quantitative models or AI initiatives. We want to see how your skills align with what we do at Goldman Sachs!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you're passionate about private equity and data science. Share specific examples of how you've used your programming skills to solve complex problems – we love a good story!
Showcase Your Technical Skills: Don’t forget to mention your programming prowess! Whether it's Python, SQL, or your favourite data science libraries, make sure we know how you’ve applied these skills in real-world scenarios. We’re all about that data-driven approach!
Apply Through Our Website: We encourage you to apply through our website for a smoother process. It’s the best way for us to receive your application and get you into our system. Plus, you’ll find all the details you need about the role there!
How to prepare for a job interview at WeAreTechWomen
✨Know Your Data Science Stuff
Make sure you brush up on your quantitative methods and programming skills, especially in Python and SQL. Be ready to discuss specific projects where you've applied these skills, as well as any data science libraries you've used like pandas or scikit-learn.
✨Understand the Investment Process
Familiarise yourself with the full lifecycle of the investment process, particularly in private equity. Be prepared to talk about how your data-driven models can enhance origination, due diligence, and investment performance.
✨Showcase Your Communication Skills
Since collaboration is key in this role, practice explaining complex data analyses in simple terms. Think of examples where you've successfully communicated insights to non-technical stakeholders, as this will demonstrate your ability to partner with deal teams and management.
✨Stay Current with AI Trends
Keep yourself updated on the latest developments in AI and machine learning. Be ready to discuss how these advancements can be leveraged in the investment space, and share any personal projects or research that showcase your passion for continuous learning in this field.