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- Tasks: Build and deploy impactful machine learning models that drive real-world decisions.
- Company: Leading SaaS firm revolutionising the automotive industry with data and AI.
- Benefits: Competitive salary, bonus, hybrid work model, and opportunities for professional growth.
- Why this job: Make a tangible impact on pricing and market behaviour in a dynamic environment.
- Qualifications: Strong experience in machine learning and proven track record in production environments.
- Other info: Collaborative culture with a focus on innovation and real-world applications.
The predicted salary is between 90000 - 90000 £ per year.
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 doesnt 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 in Manchester 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 in Manchester
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend meetups, and connect with professionals on LinkedIn. You never know who might have the inside scoop on job openings or can refer you directly.
✨Tip Number 2
Showcase your work! Create a portfolio that highlights your best projects, especially those related to machine learning and data science. This gives potential employers a taste of what you can do and how you can impact their business.
✨Tip Number 3
Prepare for interviews by practising common data science questions and case studies. Make sure you can explain your models and decisions clearly, especially to non-technical folks. Remember, communication is key!
✨Tip Number 4
Don’t forget to apply through our website! We’ve got loads of opportunities that might be perfect for you. Plus, it’s a great way to get noticed by our hiring team directly.
We think you need these skills to ace Senior Data Scientist in Manchester
Some tips for your application 🫡
Tailor Your CV: Make sure your CV speaks directly to the role of Senior Data Scientist. Highlight your experience with machine learning and statistical modelling, and don’t forget to mention any projects where your models made a real impact.
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 skills align with our mission. Be specific about your experience in commercial environments and how you've driven decisions with data.
Showcase Your Projects: If you’ve built models that are currently in production, we want to hear about them! Include links to your GitHub or any relevant portfolios that showcase your work, especially those that demonstrate your ability to communicate complex outputs clearly.
Apply Through Our Website: We encourage you to apply through our website for a smoother application process. It helps us keep track of your application and ensures you don’t miss out on any important updates from us!
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 how they were built, the data used, and the impact they had on business decisions. This shows you not only understand the technical side but also how your work drives real-world results.
✨Brush Up on Communication Skills
Since you'll be working with non-technical stakeholders, practice explaining complex concepts in simple terms. Use examples from your past experiences where you successfully communicated model outcomes to a diverse audience. This will demonstrate your ability to bridge the gap between data science and business.
✨Familiarise Yourself with the Company’s Products
Research the company’s decisioning platform and understand how it operates within the automotive ecosystem. Knowing their products will help you tailor your answers and show genuine interest in how your role as a Senior Data Scientist can contribute to their success.
✨Prepare for Technical Questions
Expect questions on Python, R, and MLOps practices. Brush up on your knowledge of model monitoring and deployment pipelines. Being able to discuss these topics confidently will highlight your technical expertise and readiness for the role.