At a Glance
- Tasks: Lead data science projects and develop machine learning solutions for financial services.
- Company: Boutique financial consulting firm with a collaborative culture.
- Benefits: Competitive salary, flexible working, and opportunities for professional growth.
- Other info: Encouraging diverse talent and supporting accessibility needs.
- Why this job: Shape the future of finance with impactful data science in a high-performing team.
- Qualifications: Experience in Python, machine learning, and cloud platforms like Azure or AWS.
The predicted salary is between 60000 - 80000 € per year.
We're partnered with a boutique financial consulting firm seeking experienced Data Scientists to join their growing data practice in London. Known for their collaborative culture and high-impact work with leading financial institutions, this is a rare opportunity to take ownership of complex data science challenges while shaping the direction of a high-performing team.
You'll lead end-to-end delivery of machine learning solutions - from early proof-of-concept through to production - working alongside engineers, domain experts, and business stakeholders across the financial services sector.
What you'll be doing:
- Taking ownership of data science projects from initial concept and rapid prototyping through to live production deployment
- Building and iterating on machine learning models that address real-world business problems across financial services
- Partnering with engineering, business, and domain teams to bridge the gap between commercial goals and technical solutions
- Serving as the go-to expert on ML system design, model tuning, and bringing solutions to production at scale
What we're looking for:
- Proven ability to build and ship production-ready data science solutions using Python and the wider ML ecosystem
- Deep practical knowledge of applied machine learning, spanning model development through to data engineering
- Comfortable working across major cloud platforms (Azure, AWS or GCP) with hands-on exposure to tools like Spark, Hive or Redshift
- A track record of leading cross-functional teams and communicating technical concepts to non-technical audiences
- Experience with MLOps practices - CI/CD, model monitoring, DevOps integration
We encourage underrepresented talent to apply to all our roles & support accessibility needs.
Data Scientist in Slough employer: Primis
Join a boutique financial consulting firm in London that champions a collaborative culture and offers high-impact work with leading financial institutions. As a Data Scientist, you'll have the unique opportunity to take ownership of complex data science projects, driving innovation while working alongside talented engineers and domain experts. With a strong focus on employee growth and a commitment to diversity, this firm not only values your contributions but also supports your professional development in a dynamic and inclusive environment.
StudySmarter Expert Advice🤫
We think this is how you could land Data Scientist in Slough
✨Tip Number 1
Network like a pro! Reach out to your connections in the financial services sector and let them know you're on the lookout for Data Scientist roles. A personal recommendation can go a long way in landing that interview.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your machine learning projects, especially those that solve real-world business problems. This will give potential employers a taste of what you can bring to their team.
✨Tip Number 3
Prepare for the technical interview! Brush up on your Python skills and be ready to discuss your experience with cloud platforms and MLOps practices. We want to see how you tackle complex data challenges!
✨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 love hearing from passionate candidates who are eager to join our collaborative culture.
We think you need these skills to ace Data Scientist in Slough
Some tips for your application 🫡
Tailor Your CV:Make sure your CV reflects the skills and experiences that match the Data Scientist role. Highlight your experience with machine learning, Python, and any cloud platforms you've worked with. We want to see how you can bring value to our team!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to tell us why you're passionate about data science and how your background aligns with our needs. Be sure to mention any relevant projects or achievements that showcase your expertise.
Showcase Your Projects:If you've got a portfolio of projects, don’t hold back! Include links to any relevant work that demonstrates your ability to build and ship production-ready solutions. We love seeing real-world applications of your skills!
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 us you’re keen on joining our collaborative culture!
How to prepare for a job interview at Primis
✨Know Your Stuff
Make sure you brush up on your machine learning concepts and Python skills. Be ready to discuss specific projects you've worked on, especially those that involved building and deploying models in production. This will show your depth of knowledge and practical experience.
✨Showcase Collaboration Skills
Since the role involves working with cross-functional teams, be prepared to share examples of how you've successfully collaborated with engineers and business stakeholders. Highlight any experiences where you bridged the gap between technical and non-technical audiences.
✨Demonstrate Problem-Solving Abilities
Think of real-world business problems you've tackled using data science. Be ready to explain your thought process, the challenges you faced, and how you overcame them. This will illustrate your ability to take ownership of complex projects.
✨Familiarise Yourself with MLOps
Since MLOps practices are crucial for this role, make sure you understand CI/CD, model monitoring, and DevOps integration. Be prepared to discuss how you've implemented these practices in past projects, as it will show you're ready to hit the ground running.
