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- Tasks: Validate AI models, ensuring their performance and reliability while collaborating with developers.
- Company: Join Goldman Sachs, a leading global investment banking firm with a diverse culture.
- Benefits: Enjoy competitive pay, professional growth opportunities, and a commitment to diversity and inclusion.
- Why this job: Make a real impact in AI model validation at a prestigious firm.
- Qualifications: Degree in a quantitative field and programming skills in Python required.
- Other info: Dynamic team environment with excellent career advancement potential.
The predicted salary is between 43200 - 72000 £ per year.
Goldman Sachs's Model Risk Management (MRM) team plays a critical role in ensuring the safety and soundness of the firm's models. MRM validates a diverse set of models, including AI and machine learning models, used within the firm. This role offers the opportunity to significantly contribute to the firm's overall model risk management framework and AI.
The Model Risk Management (MRM) group is a multidisciplinary group of quantitative experts at Goldman Sachs with presence in New York, Dallas, London, Birmingham, Warsaw, Hong Kong, and Bangalore. The MRM group is responsible for independent oversight of Model Risk at the firm, ensuring compliance with Firmwide Policy on Model Control and related standards, including documentation to evidence effective challenge over the Model development, implementation, and usage of Models.
Responsibilities:
- Independently validate the performance, accuracy, and reliability of AI models used within Goldman Sachs, focusing on aspects such as accuracy, explainability, model design, and algorithmic robustness.
- Develop and implement validation methodologies and benchmark models tailored to the specific characteristics of AI models.
- Conduct thorough testing and analysis of model outputs, identifying and documenting potential risks and limitations.
- Collaborate with model developers and business stakeholders to address identified issues and improve model performance.
Preferred Qualifications:
- A Bachelor, Master or Ph.D. degree in Computer Science, Mathematics, Physics, Engineering, or a closely related quantitative field.
- Programming expertise in Python, including experience with relevant data science libraries (e.g., NumPy, Pandas, TensorFlow, Pytorch).
- Understanding of statistical modelling and machine learning algorithms.
- Excellent analytical, problem-solving, and communication skills.
- Demonstrated curiosity, ownership, and a willingness to work in a collaborative environment.
At Goldman Sachs, we commit our people, capital and ideas to help our clients, shareholders and the communities we serve to grow. Founded in 1869, we are a leading global investment banking, securities and investment management firm. Headquartered in New York, we maintain offices around the world. We believe who you are makes you better at what you do. We're committed to fostering and advancing diversity and inclusion in our own workplace and beyond by ensuring every individual within our firm has a number of opportunities to grow professionally and personally, from our training and development opportunities and firmwide networks to benefits, wellness and personal finance offerings and mindfulness programs.
We’re committed to finding reasonable accommodations for candidates with special needs or disabilities during our recruiting process.
Risk, AI Model Validation, Vice President, Birmingham employer: Goldman Sachs
Contact Detail:
Goldman Sachs Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Risk, AI Model Validation, Vice President, Birmingham
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, especially those at Goldman Sachs. A friendly chat can open doors and give you insights that job descriptions just can't.
✨Tip Number 2
Prepare for interviews by practising common questions related to AI model validation. We recommend using the STAR method (Situation, Task, Action, Result) to structure your answers and showcase your skills effectively.
✨Tip Number 3
Showcase your passion for AI and risk management during interviews. Share any personal projects or experiences that highlight your expertise in Python and machine learning. It’s all about demonstrating your curiosity and ownership!
✨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 serious about joining the Goldman Sachs team.
We think you need these skills to ace Risk, AI Model Validation, Vice President, Birmingham
Some tips for your application 🫡
Tailor Your Application: Make sure to customise your CV and cover letter for the Risk, AI Model Validation role. Highlight your relevant experience in model validation and any specific projects that showcase your skills in AI and machine learning.
Showcase Your Skills: Don’t just list your qualifications; demonstrate how your programming expertise in Python and understanding of statistical modelling can add value to our team. Use examples from your past work to illustrate your analytical and problem-solving abilities.
Be Clear and Concise: When writing your application, keep it straightforward and to the point. We appreciate clarity, so avoid jargon and ensure your key points stand out. This will help us quickly see why you’re a great fit for the role.
Apply Through Our Website: We encourage you to submit your application through our website. It’s the best way for us to receive your details and ensures you’re considered for the position. Plus, you’ll find all the info you need about the role there!
How to prepare for a job interview at Goldman Sachs
✨Know Your Models Inside Out
Make sure you have a solid understanding of the AI models you'll be validating. Brush up on their performance metrics, explainability, and any potential risks. Being able to discuss these aspects confidently will show that you're not just familiar with the theory but can apply it practically.
✨Showcase Your Programming Skills
Since programming in Python is crucial for this role, be prepared to discuss your experience with relevant libraries like NumPy and TensorFlow. You might even want to bring examples of past projects where you've used these tools to validate models or conduct analyses.
✨Prepare for Problem-Solving Questions
Expect questions that test your analytical and problem-solving skills. Think of scenarios where you've identified model limitations or improved performance. Use the STAR method (Situation, Task, Action, Result) to structure your answers clearly and effectively.
✨Emphasise Collaboration and Communication
This role involves working closely with model developers and stakeholders. Be ready to share examples of how you've successfully collaborated in the past. Highlight your communication skills, especially in explaining complex concepts to non-technical audiences.
