At a Glance
- Tasks: Design and build Python-based tools for data-driven research in finance.
- Company: Renowned quantitative investment firm with a focus on technology and innovation.
- Benefits: Competitive salary, flexible working options, and opportunities for professional growth.
- Other info: Collaborative environment with a focus on cutting-edge research and technology.
- Why this job: Join a dynamic team and make an impact in the world of data and finance.
- Qualifications: Strong Python skills and experience with large-scale data sets.
The predicted salary is between 70000 - 85000 £ per year.
Our client is a highly regarded quantitative investment firm seeking a Research Engineer to join a growing technology team focused on enabling data-driven research.
This position centres on developing Python-based tooling, data infrastructure, and workflow frameworks that allow researchers to efficiently work with large-scale financial and alternative datasets.
The ideal candidate will have experience building research platforms, orchestrating complex data workflows, and supporting machine learning research environments.
A strong focus on data quality, MLOps, and GPU-enabled computing is essential.
Key Responsibilities
- Design, build, and maintain Python-based research tools and infrastructure.
- Develop scalable frameworks to support data analysis, feature generation, and research workflows.
- Curate and manage large, complex data-sets, ensuring high standards of quality, consistency, and accessibility.
- Enhance data pipelines for ingestion, validation, transformation, and distribution.
- Collaborate closely with research teams to translate requirements into robust engineering solutions.
- Support machine learning operations, including automation, experiment tracking, and reproducibility.
- Optimise GPU-enabled environments to improve research efficiency and performance.
- Work alongside platform and infrastructure teams to ensure systems are scalable, reliable, and well-governed.
- Required Experience
- Strong software engineering background with Python as a core development language.
- Experience building research platforms, data infrastructure, or data-intensive applications.
- Proven experience working with large-scale data-sets in a research, quantitative, or analytical environment.
- Strong understanding of data management, governance, and quality control.
- Experience designing and supporting orchestration frameworks and large-scale data processing workflows.
- Familiarity with MLOps principles, tooling, and best practices.
- Experience supporting GPU-based workloads for machine learning or research applications.
- Excellent problem-solving skills and strong attention to detail.
- Ability to work effectively with researchers, data scientists, and engineering teams.
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Senior Research Engineer employer: Selby Jennings
Selby Jennings is an exceptional employer, offering a dynamic work culture that fosters collaboration and innovation in the heart of London. Employees benefit from extensive growth opportunities, competitive compensation, and a supportive environment that encourages professional development, making it an ideal place for those looking to make a meaningful impact in the finance technology sector.
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We think this is how you could land Senior Research Engineer
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We think you need these skills to ace Senior Research Engineer
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 Selby Jennings, 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 Selby Jennings. 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 Selby Jennings
✨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 Selby Jennings!
✨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.
