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- Tasks: Own the full lifecycle of machine learning models and deliver actionable insights.
- Company: Dynamic financial services firm focused on innovation and collaboration.
- Benefits: Competitive salary, flexible working options, and opportunities for professional growth.
- Other info: Join a team that values mentorship and continuous learning in a fast-paced environment.
- Why this job: Make a real impact by leveraging data science to drive investment decisions.
- Qualifications: Experience in data science, strong Python and SQL skills, and a collaborative mindset.
The predicted salary is between 60000 - 80000 £ per year.
Overview
- End-to-End Model Ownership: Own the full lifecycle of machine learning and analytics solutions, from sourcing and validating financial and alternative datasets through to building, deploying, and maintaining production-grade models that support investment, risk, or operational decisions.
- Analytical Rigor & Validation: Design and execute robust analytical experiments with clearly defined success criteria, ensuring models are statistically sound, explainable, and deliver measurable value to investment performance, risk management, or operational efficiency.
- Stakeholder Partnership & Communication: Act as a trusted partner to investment professionals, risk, and operations teams by translating complex technical insights into clear, actionable outputs that align with regulatory constraints and commercial objectives.
- Operational Excellence & Scalability: Enhance existing data and modelling platforms through disciplined feature engineering, performance monitoring, and close collaboration with engineering teams to ensure solutions are resilient, auditable, and scalable within an enterprise environment.
- Strategic Insight & Opportunity Identification: Proactively identify opportunities where data science and automation can improve alpha generation, portfolio construction, client insights, or process efficiency, aligning initiatives with long-term firm strategy.
- Team & Industry Contribution: Maintain high standards for model development, documentation, and governance, contribute to knowledge sharing and mentorship, and selectively adopt modern analytics and AI techniques where they add real investment or operational value.
Requirements
- Demonstrated experience applying data science or machine learning to real-world problems, ideally within financial services, investment management, or other regulated environments.
- Strong Python capability, with hands-on use of analytical and ML libraries (e.g. pandas, NumPy, scikit-learn or equivalent).
- Solid SQL skills and experience working with large, structured financial or transactional datasets.
- Sound understanding of core machine learning concepts, including model development, validation, feature engineering, and performance monitoring.
- Exposure to generative AI or LLM-based applications, such as prompt design, evaluation of model outputs, or integration of third-party APIs into analytical workflows.
- Familiarity with software engineering best practices, including version control (Git), testing, and writing reproducible, well-documented code.
- Ability to structure ambiguous investment or business questions into rigorous, data-driven analyses with clear metrics and outcomes.
- Strong collaborative mindset, comfortable working across investment, risk, technology, and business teams.
- Clear and confident communication skills, with the ability to explain technical trade-offs and limitations to non-technical stakeholders.
Data Scientist employer: Glocomms
Contact Detail:
Glocomms Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Scientist
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and connect with potential colleagues on LinkedIn. We all know that sometimes it’s not just what you know, but who you know that can help you land that dream job.
✨Tip Number 2
Prepare for those interviews by brushing up on your technical skills and understanding the latest trends in data science. We recommend doing mock interviews with friends or using online platforms to get comfortable with common questions and scenarios.
✨Tip Number 3
Showcase your projects! Whether it's through a portfolio or GitHub, let your work speak for itself. We love seeing real-world applications of your skills, so make sure to highlight any relevant projects that demonstrate your analytical prowess.
✨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, we’re always looking for passionate individuals who are ready to make an impact in the data science field.
We think you need these skills to ace Data Scientist
Some tips for your application 🫡
Tailor Your CV: Make sure your CV speaks directly to the Data Scientist role. Highlight your experience with machine learning, Python, and SQL, and don’t forget to mention any relevant projects or achievements that showcase your analytical skills.
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you’re passionate about data science and how your skills align with our needs. Be sure to mention your experience in financial services or regulated environments if applicable.
Showcase Your Projects: If you've worked on any interesting data science projects, make sure to include them in your application. Whether it's a personal project or something from a previous job, demonstrating your hands-on experience can really set you apart.
Apply Through Our Website: We encourage you to apply through our website for the best chance of getting noticed. It’s super easy, and you’ll be able to keep track of your application status. Plus, we love seeing applications come directly from our site!
How to prepare for a job interview at Glocomms
✨Know Your Data Science Stuff
Make sure you brush up on your data science and machine learning concepts. Be ready to discuss your experience with Python, SQL, and any relevant libraries like pandas or scikit-learn. Prepare examples of how you've applied these skills to real-world problems, especially in financial services.
✨Showcase Your Analytical Skills
Be prepared to talk about how you design and execute analytical experiments. Think of specific instances where you defined success criteria and ensured your models were statistically sound. This will demonstrate your analytical rigor and ability to deliver measurable value.
✨Communicate Like a Pro
Practice explaining complex technical insights in simple terms. You’ll need to show that you can translate your findings into actionable outputs for non-technical stakeholders. Think of examples where you’ve successfully communicated with investment professionals or risk teams.
✨Collaborate and Contribute
Highlight your collaborative mindset and any experiences working across different teams. Discuss how you’ve contributed to knowledge sharing or mentorship in previous roles. This shows you’re not just a lone wolf but someone who values teamwork and industry contribution.