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- Tasks: Develop and maintain data science solutions using Python while solving real business problems.
- Company: Dynamic company in Nottingham with a focus on data-driven decision-making.
- Benefits: Hybrid work, 25 days holiday, yearly bonus, and contributory pension scheme.
- Other info: Flexible working with opportunities for continuous improvement and career growth.
- Why this job: Join a small team and make an impact with award-winning machine learning models.
- Qualifications: 3+ years as a data scientist with strong Python and SQL skills.
The predicted salary is between 40000 - 50000 £ per year.
We are seeking a mid-level Data Scientist with strong experience in Python for a new role in Nottingham. You will be part of a small, but close team that covers Data Science, Business Intelligence and Analytics. It is hybrid, 2 days a month in the office (so very flexible), but we are looking for someone with a sensible commute to Nottingham.
In this position you will work across a range of analytical and machine learning tasks, partnering with stakeholders to solve real business problems and support data-driven decision-making. The business has 4-6 ML models in production, one of which is award-winning. Generally, these models are in the finance and risk areas - any experience of this would be a bonus, but certainly not essential.
In addition, they have a number of side projects for you to get involved with, working with colleagues to deliver advanced analytics and insights. This is a perfect role for someone who can engage confidently with stakeholders, work both independently and within a small team on defined analytical problems, can clearly communicate insights, and is comfortable contributing to production-ready data science solutions.
- Develop, test, and maintain data science solutions using Python
- Perform exploratory data analysis (EDA) to identify trends, patterns, and opportunities
- Apply statistical and machine learning techniques to address business questions
- Support model validation, documentation, and ongoing monitoring
- Contribute to continuous improvement of data science standards, tools, and ways of working
Requirements:
- 3 plus years in industry as a data scientist
- Strong Python proficiency
- Solid understanding of statistics and machine learning fundamentals
- Experience working with structured data and SQL
- Experience supporting machine learning models in production environments
Benefits:
- Hybrid working arrangements (requirement to travel to the office 2 days per month)
- 25 days holiday + bank holidays
- Yearly bonus scheme
- Contributory Pension scheme
Data Marketing Scientist in Nottingham employer: Oscar Technology
Contact Detail:
Oscar Technology Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Marketing Scientist in Nottingham
✨Tip Number 1
Network like a pro! Reach out to people in the industry on LinkedIn or at local meetups. You never know who might have the inside scoop on job openings or can put in a good word for you.
✨Tip Number 2
Prepare for those interviews! Brush up on your Python skills and be ready to discuss your experience with machine learning models. Practise explaining complex concepts in simple terms, as you'll need to engage confidently with stakeholders.
✨Tip Number 3
Showcase your projects! If you've worked on any relevant data science projects, make sure to highlight them during interviews. Bring along examples of your exploratory data analysis or any machine learning models you've developed.
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you're genuinely interested in joining our team and contributing to our data-driven decision-making.
We think you need these skills to ace Data Marketing Scientist in Nottingham
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to the Data Scientist role. Highlight your Python skills and any experience with machine learning models, especially if you've worked in finance or risk. We want to see how your background fits with what we do!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you're excited about this role and how you can contribute to our team. Be sure to mention your experience with data analysis and stakeholder engagement.
Showcase Your Projects: If you've worked on any relevant projects, whether in a professional setting or as personal endeavours, make sure to include them. We love seeing practical applications of your skills, especially those that demonstrate your problem-solving abilities.
Apply Through Our Website: We encourage you to apply through our website for a smoother process. It helps us keep track of your application and ensures you get all the updates directly from us. Plus, it shows you're keen on joining our team!
How to prepare for a job interview at Oscar Technology
✨Know Your Python Inside Out
Since the role requires strong Python proficiency, make sure you brush up on your skills. Be prepared to discuss specific projects where you've used Python for data analysis or machine learning. Practising coding challenges can also help you feel more confident.
✨Showcase Your Analytical Mindset
Prepare to talk about your experience with exploratory data analysis (EDA). Think of examples where you've identified trends or patterns that led to actionable insights. This will demonstrate your ability to tackle real business problems effectively.
✨Engage with Stakeholders
This position involves working closely with stakeholders, so practice how you communicate complex data insights in a clear and engaging way. Consider role-playing with a friend to simulate explaining your findings to someone without a technical background.
✨Familiarise Yourself with Machine Learning Models
Even if you don't have direct experience with finance and risk models, understanding the basics of machine learning and how models are validated and monitored will be beneficial. Be ready to discuss any relevant projects or side projects you've worked on that involved machine learning.