Overview
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?
- Must-Have: 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?
What is in it for you?
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 mutual.
- Leaders who support your development through coaching and managing opportunities.
- Opportunities to work with the best in the field.
- Ability to make a difference and lasting impact.
- Work in a dynamic, collaborative, progressive, and high-performing team.
Agency Notice
RBC Group does not accept agency resumés. Please do not forward resumés to our employees, nor any other company location. RBC Group only pay fees to agencies where they have entered into a prior agreement to do so and in any event do not pay fees related to unsolicited resumés. Please contact the Recruitment function for additional details.
Job Skills
Big Data Management, Data Mining, Data Science, Deep Learning, Machine Learning (ML), Predictive Analytics, Programming Languages
Additional Job Details
Address: 100 BISHOPSGATE:LONDONCity: LondonCountry: United KingdomWork hours/week: 35Employment Type: Full timePlatform: WEALTH MANAGEMENTJob Type: RegularPay Type: SalariedPosted Date: 2025-11-10Application Deadline: 2026-04-10
Note: Applications will be accepted until 11:59 PM on the day prior to the application deadline date above
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