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- Tasks: Transform complex data challenges into actionable predictive models and drive impactful decisions.
- Company: Join a forward-thinking team at RBC, where people matter as much as numbers.
- Benefits: Enjoy flexible work arrangements, competitive salary, and opportunities for professional growth.
- Why this job: Make a real impact in wealth management while developing your skills in a dynamic environment.
- Qualifications: Experience in machine learning, collaboration skills, and a degree in a quantitative field.
- Other info: Mentorship opportunities and a culture of shared success await you.
The predicted salary is between 55000 - 70000 £ per year.
We're on the hunt for a Data Science Maverick to bridge the gap between cutting‑edge AI and real‑world business impact. As part of the Wealth Management Europe (WME) team, you'll be the go‑to problem solver, turning complex challenges into smart, actionable predictive models. Think of yourself as a data translator - speaking both "tech" and "business" fluently to drive decisions that matter.
What will you do?
- Be the AI Whisperer: Champion data and AI solutions across teams, from front office to compliance, making tech feel less like magic and more like a superpower.
- Solve Puzzles, Not Just Problems: Dive into messy business challenges, ask the right questions, and shape them into clear, data‑driven projects. No jargon, just results.
- End‑to‑End Ownership: From cleaning data to deploying models (with help from ML engineers - we're a team, after all!), you'll own the full lifecycle while keeping stakeholders in the loop.
- Tell Data Stories: Turn numbers into narratives that wow senior leaders, compliance teams, or fellow nerds. Your visualizations will be the star of every meeting.
- Share the Love: Mentor junior team members, foster a culture of clean code and collaboration, and ensure our models are used responsibly (governance isn't boring - it's essential!).
Why You'll Love It Here:
- Flexibility: Work from our London office 4 days a week, with 1 day remote - balance is key!
- Impact: Your work directly shapes strategies for wealth management clients across Europe.
- Grow with Us: Learn from the best, tackle diverse projects, and develop skills beyond your comfort zone.
- Team Vibes: Collaborative, dynamic, and high‑performing - no silos, just shared wins.
What do you need to succeed?
- Proven experience building machine learning models and deploying them into production.
- Demonstrated ability to collaborate with cross‑functional teams and translate business needs into data‑driven solutions.
- A degree (or equivalent experience) in a quantitative field -- stats, CS, math, or similar.
- Strong understanding of agile methodologies, DevOps, and MLOps principles.
- Experience navigating data governance, security, and compliance in machine learning projects.
- Portfolio or examples of delivering data science solutions to non‑technical stakeholders.
Bonus Points:
- Mentoring experience, familiarity with BI tools (PowerBI, Tableau), or experience in structured organizations with a collaborative mindset.
Why RBC? We're not just about numbers— we're about people. Grow your career, make a difference, and work with a team that values bold ideas and mutual success. Ready to build the future of wealth management? We thrive on the challenge to be our best - progressive thinking to keep growing and working together to deliver trusted advice to help our clients thrive and communities prosper. We care about each other, reaching our potential, making a difference to our communities, and achieving success that is.
Data Scientist in London employer: Royal Bank of Canada
Contact Detail:
Royal Bank of Canada Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Scientist in London
✨Tip Number 1
Network like a pro! Get out there and connect with folks in the industry. Attend meetups, webinars, or even casual coffee chats. You never know who might have the inside scoop on job openings or can put in a good word for you.
✨Tip Number 2
Show off your skills! Create a portfolio that highlights your best data science projects. Make sure to include examples that demonstrate your ability to turn complex data into actionable insights. This will help you stand out when chatting with potential employers.
✨Tip Number 3
Practice your pitch! Be ready to explain how you can bridge the gap between tech and business. Think about how your experience aligns with the role of a Data Scientist and be prepared to share specific examples during interviews.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you’re genuinely interested in joining our team and making an impact in wealth management.
We think you need these skills to ace Data Scientist in London
Some tips for your application 🫡
Show Your Data Science Skills: Make sure to highlight your experience with machine learning models and how you've deployed them in the past. We want to see your problem-solving skills in action, so include specific examples that showcase your ability to turn complex challenges into actionable insights.
Speak Our Language: When writing your application, remember to use clear and straightforward language. Avoid jargon and focus on how your skills can bridge the gap between tech and business. We love a good data story, so make sure to tell us how you've communicated your findings to non-technical stakeholders.
Tailor Your Application: Take the time to customise your application for this role. Mention how your background aligns with our needs in wealth management and data governance. We appreciate candidates who show they've done their homework and understand what we're all about!
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way for us to keep track of your application and ensure it gets the attention it deserves. Plus, it shows you're serious about joining our team!
How to prepare for a job interview at Royal Bank of Canada
✨Know Your Data Inside Out
Before the interview, make sure you’re well-versed in your past projects. Be ready to discuss specific machine learning models you've built, the challenges you faced, and how you deployed them. This will show that you can bridge the gap between tech and business effectively.
✨Speak Their Language
Since the role requires translating complex data into actionable insights, practice explaining your work in simple terms. Prepare examples of how you've communicated technical concepts to non-technical stakeholders, as this will demonstrate your ability to be a 'data translator'.
✨Showcase Your Storytelling Skills
Prepare to present your data visualisations and explain the narratives behind them. Think about how your insights have driven decisions in the past and be ready to share these stories. This will highlight your ability to turn numbers into compelling narratives that resonate with senior leaders.
✨Emphasise Collaboration
Highlight your experience working with cross-functional teams. Be prepared to discuss how you’ve collaborated with ML engineers or other departments to ensure successful project outcomes. This will show that you value teamwork and understand the importance of keeping stakeholders in the loop.