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- Tasks: Develop and implement advanced machine learning models for impactful projects.
- Company: Join Cerberus, a global investment platform with a startup vibe.
- Benefits: Competitive salary, flexible work environment, and opportunities for professional growth.
- Why this job: Make a real difference by translating data into actionable insights that drive business value.
- Qualifications: Strong statistical knowledge and experience in machine learning.
- Other info: Fast-paced, agile environment with continuous learning and collaboration.
The predicted salary is between 36000 - 60000 £ per year.
As a Machine Learning Scientist on the AI team at Cerberus, you’ll work on high-impact projects that combine the pace of a startup with the reach of a global investment platform. Our team partners directly with internal investment desks as well as portfolio companies across industries to deliver machine learning solutions that unlock value and accelerate decision-making.
Your work will range from developing and validating robust predictive models for pricing and valuation across diverse asset classes to dynamically optimizing prices under changing market conditions. You’ll be expected to translate complex data into actionable insights and ensure your solutions are not only technically sound but also adopted and delivering measurable business value, supporting deal team members and portfolio company executives.
We’re looking for machine learning scientists who are passionate about impact—those who bring deep statistical knowledge, thrive in fast-paced environments, and want to see their models deployed, used, and making a difference.
What you will do:- Build and deliver AI solutions: Design and implement advanced models and systems as both an individual contributor and as part of cross-functional teams.
- Drive impact through execution: Apply a hypothesis-driven approach to design solutions, collaborate with technical teams, and deliver results that create measurable business value.
- Work in an agile, fast-paced environment: Rapidly iterate and adapt to changing priorities, using creativity and pragmatism to maximize outcomes.
- Leverage modern tools and methods: Develop innovative solutions using contemporary platforms, languages, and frameworks, and package IP into reusable components.
- Communicate insights effectively: Translate complex technical concepts into clear, compelling narratives that drive understanding and action across technical and non-technical audiences.
- Build trust through delivery: Establish credibility by delivering high-quality solutions, challenging assumptions constructively, and iterating quickly in response to feedback.
- Develop broad technical capability: Work across the full data science lifecycle, continuously learning and applying new technologies.
Real estate portfolio valuation: Work on developing advanced valuation models for real estate portfolios using internal and external data sources. This includes building predictive models with uncertainty estimates, improving model performance through rigorous evaluation, and creating data pipelines to support modelling and analytics. You’ll also prototype processes downstream of valuation models, such as optimization approaches, to enhance pricing strategies, collaborating with stakeholders to integrate these solutions into business processes.
Machine Learning Scientist employer: Cerberus Capital Management
Contact Detail:
Cerberus Capital Management Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Scientist
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend meetups, and connect with fellow machine learning enthusiasts. You never know who might have the inside scoop on job openings or can refer you directly.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your machine learning projects. Whether it's predictive models or data visualisations, having tangible examples of your work can really set you apart during interviews.
✨Tip Number 3
Prepare for those technical interviews! Brush up on your algorithms and statistical knowledge, and practice coding challenges. Being able to demonstrate your problem-solving skills under pressure is key to landing that role.
✨Tip Number 4
Don’t forget to apply through our website! We’re always on the lookout for passionate machine learning scientists. Your next big opportunity could be just a click away, so make sure to check it out regularly!
We think you need these skills to ace Machine Learning Scientist
Some tips for your application 🫡
Tailor Your CV: Make sure your CV reflects the skills and experiences that align with the Machine Learning Scientist role. Highlight any relevant projects or technologies you've worked with, especially those that demonstrate your ability to deliver impactful AI solutions.
Craft a Compelling Cover Letter: Use your cover letter to tell us why you're passionate about machine learning and how you can contribute to our team. Share specific examples of your work that showcase your problem-solving skills and ability to translate complex data into actionable insights.
Showcase Your Technical Skills: Don’t forget to mention the modern tools and methods you’re familiar with. Whether it’s programming languages, frameworks, or platforms, let us know how you’ve used these in past projects to drive measurable business value.
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you don’t miss out on any important updates during the process!
How to prepare for a job interview at Cerberus Capital Management
✨Know Your Models Inside Out
Make sure you can discuss your previous machine learning projects in detail. Be ready to explain the models you used, why you chose them, and how they performed. This shows your deep statistical knowledge and helps demonstrate your ability to translate complex data into actionable insights.
✨Showcase Your Problem-Solving Skills
Prepare to discuss specific challenges you've faced in past projects and how you overcame them. Use a hypothesis-driven approach to illustrate your thought process. This will highlight your ability to drive impact through execution and adapt to changing priorities.
✨Communicate Clearly and Effectively
Practice explaining technical concepts in simple terms. You’ll need to communicate with both technical and non-technical audiences, so being able to craft compelling narratives around your work is crucial. Think about how you can make your insights resonate with different stakeholders.
✨Demonstrate Your Agility
Be prepared to discuss how you've worked in fast-paced environments before. Share examples of how you rapidly iterated on projects or adapted to new information. This will show that you thrive under pressure and can maximise outcomes even when priorities shift.
