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For Companies At a Glance
- Tasks: Build and improve risk-scoring models to assess customer creditworthiness.
- Company: Dynamic start-up in the Global IT sector with a focus on innovation.
- Benefits: Negotiable salary, share options, and exciting benefits coming soon.
- Other info: Collaborative environment with opportunities for growth and development.
- Why this job: Make a real impact by turning complex data into actionable insights.
- Qualifications: 3+ years in Data Science, strong Python and SQL skills required.
The predicted salary is between 50000 - 65000 £ per year.
Your Role
As a Data Scientist focused on risk modelling, you will be responsible for building, improving and monitoring scoring models used to assess customer creditworthiness across our markets. You’ll work closely with business, analytics and engineering teams to translate data into critical decisions.
What You Will Be Doing
- Develop and implement application and behavioral risk‑scoring models
- Perform feature engineering to improve predictive power and model performance
- Analyze data to identify trends and opportunities to improve scoring logic
- Collaborate with developers and business analysts to integrate models into production systems
- Monitor and analyze model performance in production and iterate for improvement
Who You Are
- Independent and proactive — you take ownership of problems and drive solutions
- Analytical thinker — you enjoy working with complex data and turning it into actionable insight
- Effective communicator — you can clearly explain models and outcomes to non‑technical stakeholders
- Curious and impact‑driven — you want your models to make a real difference
Your Experience
- 3+ years in Data Science with hands‑on experience building scoring models
- Deep knowledge of machine learning and statistical modeling techniques
- Strong Python skills (Pandas, Scikit‑learn, TensorFlow, PyTorch)
- Proficiency in feature engineering and model selection
- Strong SQL skills and experience working with large datasets
- Experience communicating with cross‑functional teams including non‑technical partners
What We Are Offering
- Competitive salary is negotiable depending on the candidate's level
- Share options
- We are still a start‑up and more benefits are on the way
Data scientist Global IT employer: Fintech Farm Ltd
Contact Detail:
Fintech Farm Ltd Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data scientist Global IT
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend meetups, and connect with fellow data enthusiasts. You never know who might have the inside scoop on job openings or can refer you directly.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your scoring models and projects. This is your chance to demonstrate your analytical thinking and technical prowess, making it easier for potential employers to see your value.
✨Tip Number 3
Prepare for interviews by brushing up on your communication skills. Practice explaining complex concepts in simple terms, as you'll need to convey your insights to non-technical stakeholders effectively.
✨Tip Number 4
Don't forget to apply through our website! We love seeing candidates who are proactive and take ownership of their job search. Plus, it gives us a chance to see your application in the best light possible.
We think you need these skills to ace Data scientist Global IT
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience in data science, especially with scoring models. We want to see how your skills align with the role, so don’t be shy about showcasing your Python and SQL expertise!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you’re passionate about risk modelling and how your analytical thinking can drive impactful solutions. Let us know how you can make a difference at StudySmarter.
Showcase Your Projects: If you've worked on relevant projects, include them in your application. We love seeing real-world examples of your work with machine learning and feature engineering. It helps us understand your hands-on experience better!
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 don’t miss out on any important updates from our team!
How to prepare for a job interview at Fintech Farm Ltd
✨Know Your Models Inside Out
Make sure you can discuss the scoring models you've built in detail. Be prepared to explain your feature engineering choices and how they improved predictive power. This shows you not only understand the technical side but also the impact of your work.
✨Brush Up on Your Python Skills
Since strong Python skills are a must, review your knowledge of libraries like Pandas, Scikit-learn, TensorFlow, and PyTorch. Be ready to discuss specific projects where you used these tools, as practical examples will impress your interviewers.
✨Communicate Clearly with Non-Technical Stakeholders
Practice explaining complex data concepts in simple terms. You’ll need to convey your findings to business and analytics teams, so being able to articulate your insights clearly is crucial. Consider preparing a few examples of how you've done this in the past.
✨Show Your Curiosity and Drive for Impact
Demonstrate your passion for making a difference through your models. Share instances where your analytical thinking led to actionable insights or improvements. This will highlight your proactive nature and alignment with the company's goals.