At a Glance
- Tasks: Transform guest data into real-time risk scores and enhance 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 enjoy a dynamic startup environment.
- Why this job: Be a founding Data Scientist and shape the future of short-term rentals.
- Qualifications: 3+ years in ML models, strong stats background, and fraud detection 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.
Data Scientist in Reading employer: Safeguest
At Safeguest, we pride ourselves on being an innovative employer that values growth and collaboration. As a founding Data Scientist, you'll have the unique opportunity to shape our data strategy from the ground up while working closely with our co-founder in a dynamic startup environment. We offer flexible part-time hours with a clear path to full-time employment, competitive equity options, and a culture that encourages ownership and impactful contributions.
StudySmarter Expert Advice🤫
We think this is how you could land Data Scientist in Reading
✨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 land you that dream job.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your data science projects, especially those related to fraud detection or risk scoring. We want to see your hands-on experience, so make sure it’s easy for us to find and understand your work.
✨Tip Number 3
Prepare for interviews by brushing up on your statistics and probability knowledge. We’ll likely dive deep into your understanding of these concepts, so be ready to discuss how they apply to real-world data challenges.
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we love seeing candidates who take the initiative to engage directly with us.
We think you need these skills to ace Data Scientist in Reading
Some tips for your application 🫡
Show Your Passion for Data:When you write your application, let your enthusiasm for data science shine through! Share specific examples of projects you've worked on that relate to guest risk scoring or fraud detection. We want to see how your experience aligns with our mission at Safeguest.
Tailor Your CV and Cover Letter:Make sure to customise your CV and cover letter for this role. Highlight your 3+ years of experience in building ML models and any relevant work in insurtech or fintech. We love seeing candidates who take the time to connect their skills to what we do!
Be Clear and Concise:Keep your application straightforward and to the point. Use bullet points where possible to make it easy for us to read. We appreciate clarity, especially when it comes to your achievements and how they relate to the role.
Apply Through Our Website:Don’t forget to apply through our website! It’s the best way for us to keep track of your application and ensure it gets the attention it deserves. Plus, it shows you’re serious about joining our team at Safeguest!
How to prepare for a job interview at Safeguest
✨Know Your Data Science Fundamentals
Brush up on your statistics and probability knowledge, as these are crucial for the role. Be prepared to discuss how you've applied these concepts in real-world scenarios, especially in fraud detection or risk scoring.
✨Showcase Your ML Experience
Come ready to talk about specific machine learning models you've built and deployed. Highlight any challenges you faced with messy data and how you overcame them, as this will demonstrate your hands-on experience.
✨Understand the Business Context
Familiarise yourself with Safeguest's mission and products. Think about how your skills can directly impact their guest verification and damage protection processes, and be ready to share ideas on improving their risk engine.
✨Demonstrate a Pragmatic Mindset
Prepare to discuss how you prioritise tasks and manage projects. Share examples of how you've taken ownership of your work and delivered measurable results, as this aligns with the ownership-driven mindset they’re looking for.
