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- Tasks: Build and deploy impactful machine learning models that drive real-world decisions.
- Company: Leading SaaS company in the automotive sector with a focus on data and AI.
- Benefits: Competitive salary up to £90,000, bonus, and hybrid working model.
- Why this job: Make a tangible impact on pricing and market behaviour with your data expertise.
- Qualifications: Strong experience in machine learning and proven track record in production environments.
- Other info: Collaborative culture with opportunities to work on large datasets across multiple markets.
This is not a role where you sit in a notebook building models no one uses. Our client is a market-leading SaaS organisation operating at scale within the automotive ecosystem, building the intelligence layer that drives real-world commercial decisions across Europe. They are investing heavily in data and AI to power a next-generation decisioning platform helping enterprise clients make high-value pricing, trading, and operational decisions across thousands of assets, in real time. This is about building models that directly influence revenue, pricing, and market behaviour.
Youll take ownership of production-grade models that sit at the core of a live product used daily by commercial teams across multiple markets. This includes:
- Pricing intelligence
- Buyer behaviour modelling
- Stock segmentation
- Recommendation systems
Youll work closely with product, engineering, and domain experts to ensure your work doesn't just function it lands, scales, and delivers measurable impact.
What Youll Be Doing
- Building and deploying machine learning models into production environments
- Designing decisioning systems that optimise pricing, channel selection, and asset performance
- Developing explainable models (SHAP, LIME, feature importance) that drive user trust
- Creating frameworks to measure real-world impact and model effectiveness
- Working with large, complex datasets across multiple markets
- Collaborating with product and engineering teams to embed models into live systems
What Were Looking For
- Strong experience in machine learning, statistical modelling, or pricing / propensity modelling
- Proven track record of delivering models that go into production and stay there
- Experience working in commercial environments where data drives decisions
- Strong Python (or R) experience across modern data science tooling
- Familiarity with MLOps, model monitoring, and deployment pipelines
- Ability to communicate complex outputs clearly to non-technical stakeholders
Senior Data Scientist employer: Daniel James Resourcing Ltd
Contact Detail:
Daniel James Resourcing Ltd Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior Data Scientist
✨Tip Number 1
Network like a pro! Reach out to people 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
Showcase your skills! Create a portfolio of your projects, especially those related to machine learning and data science. We recommend using platforms like GitHub to share your work, as it gives potential employers a glimpse into your coding style and problem-solving abilities.
✨Tip Number 3
Prepare for interviews by practising common data science questions and case studies. We suggest doing mock interviews with friends or using online resources to get comfortable discussing your experience and how it relates to real-world applications.
✨Tip Number 4
Don’t forget to apply through our website! We’ve got loads of opportunities waiting for talented individuals like you. Plus, applying directly can sometimes give you an edge over other candidates.
We think you need these skills to ace Senior Data Scientist
Some tips for your application 🫡
Show Your Passion for Data: When writing your application, let your enthusiasm for data science shine through! Share specific examples of how you've used data to drive decisions and make an impact in previous roles. We love seeing candidates who are genuinely excited about the work they do.
Tailor Your CV and Cover Letter: Make sure to customise your CV and cover letter for this role. Highlight your experience with machine learning, statistical modelling, and any relevant projects that align with our needs. We want to see how your skills fit perfectly with what we're looking for!
Be Clear and Concise: Keep your application clear and to the point. Use straightforward language to explain your experience and achievements. We appreciate candidates who can communicate complex ideas simply, especially since you'll be working with non-technical stakeholders.
Apply Through Our Website: Don't forget to apply 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 serious about joining our team at StudySmarter!
How to prepare for a job interview at Daniel James Resourcing Ltd
✨Know Your Models Inside Out
Make sure you can discuss your previous machine learning models in detail. Be ready to explain the algorithms you used, why you chose them, and how they impacted business decisions. This role is all about real-world applications, so focus on how your work has driven measurable results.
✨Brush Up on Your Python Skills
Since strong Python experience is a must, ensure you're comfortable with the latest data science libraries and tools. Practise coding challenges or projects that showcase your ability to build and deploy models. You might even want to prepare a small demo of a project you've worked on to share during the interview.
✨Prepare for Technical Questions
Expect questions that dive deep into statistical modelling and pricing strategies. Review concepts like SHAP and LIME, and be prepared to discuss how you would create explainable models. Being able to communicate complex ideas simply will impress your interviewers, especially since you'll need to engage with non-technical stakeholders.
✨Showcase Your Collaboration Skills
This role involves working closely with product and engineering teams, so be ready to discuss your experience in collaborative environments. Share examples of how you've successfully partnered with others to embed models into live systems and the impact it had on the overall project.