At a Glance
- Tasks: Design and build APIs to make ML models accessible and useful across the organisation.
- Company: Join RBC, a leading financial institution with a focus on innovation and collaboration.
- Benefits: Enjoy competitive pay, flexible benefits, and a supportive work environment.
- Other info: Opportunities for professional growth and mentorship in a high-performing team.
- Why this job: Make a real impact by working with cutting-edge technology in a dynamic team.
- Qualifications: Degree in Computer Science or related field; strong back-end development experience required.
The predicted salary is between 63000 - 77000 £ per year.
What is the opportunity? You’ll build the plumbing that gets ML models into the hands of the people who need them. Whether it’s REST APIs feeding internal apps, GraphQL endpoints powering dashboards, or MCP servers fueling AI agents - you own the serving layer that makes models actually useful across RBC. You’ll report to the Director, Data Science, working closely with the Data Science Lead to architect and deliver APIs and integrations that are reliable, secure, and built for scale. This is a permanent, full-time role requiring 4 days in the office at our London location.
What will you do?
- Design and build REST and GraphQL APIs that expose ML models from Databricks to internal apps, CRM platforms, and enterprise systems - handling contract design, versioning, authentication, and clear documentation.
- Build MCP servers that expose models and data products as structured tools for AI agents and LLM apps - enabling new use cases and unlocking what’s possible with AI.
- Create middleware layers when needed - handling transformation, enrichment, or protocol adaptation to bridge gaps between Databricks and downstream consumers.
- Keep APIs stable and well-documented - actively gather feedback from consumers (BI team, app builders, etc.) to fix gaps early and iterate based on what actually works.
- Collaborate with ML engineers and data engineers to ensure the serving layer sits on reliable, well-governed infrastructure.
- Build lightweight prototype applications to test, demonstrate, and validate model outputs and MCP-served capabilities.
- Set the bar for engineering quality - mentoring junior colleagues through code reviews and knowledge sharing; champion clean code, version control, CI/CD, and automated testing.
- Ensure security and governance standards are met across all APIs, MCP servers, and integrations.
What do you need to succeed?
- Must-have: Degree-level qualification (or equivalent professional experience) in Computer Science, Software Engineering, or related field.
- Extensive professional software engineering experience with strong emphasis on back-end development, API design, and production-grade service delivery.
- Demonstrable experience designing, building, and maintaining REST and/or GraphQL APIs - comfortable with contract design, versioning, authentication, and documentation.
- Proficiency in Python as a primary back-end language, with hands-on experience building and deploying Python-based APIs (FastAPI, Flask, or similar).
- Experience with cloud data platforms - particularly Databricks - and solid understanding of how model outputs and data products are served from these platforms.
- Demonstrable experience designing and building MCP servers, defining tools and resources, and managing server lifecycle.
- Nice-to-have: Hands-on experience with Databricks Model Serving, MLflow, or equivalent model registry and serving infrastructure.
- Familiarity with agentic application frameworks like LangChain, LlamaIndex, or AutoGen, and how they interact with MCP servers.
- Experience working in regulated industries such as financial services.
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. A comprehensive Total Rewards Programme including bonuses, flexible benefits and competitive compensation. 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. A world-class training programme in financial services.
Senior Engineer - Data Science in London employer: RBC
RBC is an excellent employer, offering a dynamic work environment in London that fosters professional growth and collaboration within a high-performing team. Employees benefit from a competitive compensation package, comprehensive development opportunities, and the chance to work on innovative projects like the ATOM application, making it a rewarding place for those passionate about finance and technology.
StudySmarter Expert Advice🤫
We think this is how you could land Senior Engineer - Data Science in London
✨Get Involved in Data Science Meetups
Tap into local data science meetups or workshops to connect with fellow enthusiasts and professionals. These events are goldmines for networking, and sometimes even lead directly to job openings at companies like RBC!
✨Show Off Your Projects
Start building a public portfolio showcasing your data science projects on platforms like GitHub or personal websites. Highlight unique analyses or models you've developed. This not only demonstrates your skills but also gets your name out there for roles like Senior Engineer - Data Science at RBC.
✨Leverage Professional Networks
Join professional bodies related to data science, like the Data Science Society or similar organisations. Getting involved can lead to mentorship opportunities and insider knowledge about full-time positions at companies like RBC.
✨Apply Directly through Our Website
When you find a suitable opening like Senior Engineer - Data Science at RBC, make sure to apply directly through our website. It gives you an edge and shows you're keen to join our team. Plus, who doesn’t love a direct application? It’s easier than navigating through job boards!
We think you need these skills to ace Senior Engineer - Data Science in London
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!
Craft a Tailored Cover Letter:For a full-time role at RBC, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at RBC. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at RBC
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
✨Showcase Your Projects
Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at RBC!
✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.