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For Companies At a Glance
- Tasks: Own the full lifecycle of machine learning and analytics solutions to drive impactful decisions.
- Company: Join a leading financial services firm focused on innovation and collaboration.
- Benefits: Competitive salary, flexible working options, and opportunities for professional growth.
- Other info: Contribute to cutting-edge projects and enhance your skills in a supportive team.
- Why this job: Make a real impact by transforming data into actionable insights in a dynamic environment.
- 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. 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 showcasing your data science projects, especially those that demonstrate your analytical rigor and model ownership. This will give you an edge when chatting with hiring managers.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and communication skills. Be ready to explain complex concepts in simple terms, as you'll need to translate insights for non-technical stakeholders.
✨Tip Number 4
Don’t forget to apply through our website! We love seeing candidates who are genuinely interested in joining our team. Plus, it’s a great way to ensure your application gets the attention it deserves.
We think you need these skills to ace Data Scientist
Some tips for your application 🫡
Showcase Your Experience: When applying, make sure to highlight your hands-on experience with data science and machine learning. We want to see how you've tackled real-world problems, especially in financial services or similar fields. Use specific examples to demonstrate your skills!
Be Clear and Concise: Keep your application straightforward and to the point. We appreciate clarity, so avoid jargon and focus on how your skills align with our needs. Remember, we’re looking for someone who can communicate complex ideas simply!
Tailor Your Application: Don’t just send a generic application! Tailor your CV and cover letter to reflect the job description. Highlight your Python and SQL skills, and mention any relevant projects that showcase your analytical rigor and operational excellence.
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it shows you’re keen on joining our team at StudySmarter!
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 Rigor
Be prepared to talk about how you design and execute analytical experiments. Highlight your ability to define success criteria and ensure that your models are statistically sound. Bring examples of past projects where you delivered measurable value through your analyses.
✨Communicate Like a Pro
Since you'll be working with various teams, practice explaining complex technical insights in simple terms. Think of ways to translate your findings into actionable outputs that align with business objectives. This will show that you can bridge the gap between technical and non-technical stakeholders.
✨Demonstrate Collaboration Skills
Emphasise your collaborative mindset during the interview. Share experiences where you've worked closely with investment, risk, and engineering teams. Highlight how you contributed to enhancing data platforms or improving operational efficiency, as this shows you're a team player who values input from others.