At a Glance
- Tasks: Develop and deploy ML models for guest risk scoring and fraud detection.
- Company: Join a pioneering startup in guest verification and damage protection.
- Benefits: Part-time role with potential for full-time, equity options, and hands-on experience.
- Other info: Work closely with the co-founder and shape the future of our data strategy.
- Why this job: Be a founding data scientist and make a real impact in a growing company.
- Qualifications: 3+ years in ML model deployment, strong stats background, and fraud/risk experience.
The predicted salary is between 30000 - 40000 £ per year.
We're bringing on our first Data Scientist — starting part-time, with a clear path to full-time as we grow. Safeguest is a guest verification and damage protection platform for short-term rental hosts. We help hosts and property managers screen guests, score risk in real time, and offer damage protection and deposit-waiver products that replace traditional security deposits. Our risk engine sits at the center of every booking decision and every product we price — and we're looking for the person who'll make it world-class.
This is a founding data role, structured to start as a focused part-time engagement (≈2–3 days/week). You'll deliver real, measurable wins in your first 90 days — and we expect this to grow into a full-time seat for the right person.
What you'll own:
- Guest risk scoring — turn verification, booking, and behavioral signals into real-time risk scores that beat our current rules
- Fraud detection — catch fake IDs, stolen cards, and bad actors before check-in, without blocking good guests
- Actuarial pricing — model claim frequency and severity to keep our protection products profitable
You'll work directly with the co-founder, build our data and modeling foundations from scratch, and ship models that measurably reduce losses.
You're a great fit if you have:
- 3+ years building and deploying ML models in production
- Strong grounding in statistics and probability
- Hands-on experience in fraud/risk, credit/underwriting, or actuarial/insurance pricing
- Comfort with messy, imbalanced, real-world data
- A pragmatic, ownership-driven mindset — you scope, prioritize, and ship
Bonus points for: insurtech / fintech / payments background, MLOps experience, or short-term rental / marketplace domain knowledge.
Why this setup: Starting part-time lets us move fast together and prove the impact — with founding-team equity and a genuine path to full-time as the role grows.
Interested, or know someone who'd be perfect? Drop a comment or DM me.
BI Data Modeler employer: Safeguest
At Safeguest, we pride ourselves on being an innovative employer that values growth and collaboration. As a founding team member in a dynamic startup environment, you'll have the unique opportunity to shape our data strategies while enjoying flexible part-time hours that can evolve into a full-time role. Our culture fosters ownership and creativity, offering you the chance to make a significant impact from day one, alongside a supportive team dedicated to redefining guest verification and damage protection in the short-term rental market.
StudySmarter Expert Advice🤫
We think this is how you could land BI Data Modeler
✨Tip Number 1
Network like a pro! Reach out to people in the industry, especially those working in insurtech or fintech. A friendly chat can lead to opportunities that aren’t even advertised yet.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your ML models and data projects. This is your chance to demonstrate how you can turn messy data into actionable insights — just what Safeguest needs!
✨Tip Number 3
Be proactive! If you see a job that fits, don’t just apply through the usual channels. Hit us up directly on our website and express your enthusiasm for the role — it shows initiative!
✨Tip Number 4
Prepare for the interview by understanding Safeguest’s mission and products. Be ready to discuss how your experience with fraud detection and risk scoring can help us build a world-class risk engine.
We think you need these skills to ace BI Data Modeler
Some tips for your application 🫡
Tailor Your CV:Make sure your CV speaks directly to the role of BI Data Modeler. Highlight your experience with ML models, fraud detection, and any relevant stats knowledge. We want to see how your skills align with our needs!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Share your passion for data science and how you can contribute to our mission at Safeguest. Be sure to mention any specific projects or achievements that relate to guest risk scoring or actuarial pricing.
Show Your Problem-Solving Skills:In your application, give examples of how you've tackled messy data or built models that made a real impact. We love a pragmatic mindset, so let us know how you prioritise and ship solutions in challenging situations.
Apply Through Our Website:We encourage you to apply through our website for a smoother process. It helps us keep track of applications and ensures you don’t miss out on any important updates from us!
How to prepare for a job interview at Safeguest
✨Know Your Data Inside Out
Make sure you’re familiar with the types of data Safeguest works with, especially in guest risk scoring and fraud detection. Brush up on your statistics and probability skills, as these will be crucial in demonstrating your ability to build and deploy ML models effectively.
✨Showcase Your Practical Experience
Prepare to discuss specific projects where you've built and deployed ML models in production. Highlight any experience you have in fraud/risk or actuarial pricing, as this will resonate well with the team looking for someone who can hit the ground running.
✨Demonstrate Ownership and Pragmatism
Be ready to share examples of how you’ve taken ownership of projects in the past. Discuss how you scope, prioritise, and ship your work, as this aligns perfectly with the ownership-driven mindset they’re looking for in a candidate.
✨Ask Insightful Questions
Prepare thoughtful questions about the company’s risk engine and their approach to data modelling. This shows your genuine interest in the role and helps you understand how you can contribute to making their systems world-class.
