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 in a fast-paced, dynamic environment.
- Why this job: Be a founding Data Scientist and shape the future of our risk engine.
- Qualifications: 3+ years in ML models, strong stats background, and fraud/risk experience.
The predicted salary is between 40000 - 50000 £ 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 Swindon 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 Swindon
✨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 projects, especially those related to fraud detection or risk scoring. This will give potential employers a taste of what you can bring to the table.
✨Tip Number 3
Prepare for interviews by brushing up on your statistics and probability knowledge. Be ready to discuss how you've tackled messy data in the past and how you’d approach building models for real-time risk scoring.
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets seen. Plus, we love seeing candidates who take the initiative to engage with us directly.
We think you need these skills to ace Data Scientist in Swindon
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 and how they relate to guest risk scoring or fraud detection. We want to see that you're not just qualified, but genuinely excited about making an impact.
Tailor Your CV and Cover Letter:Make sure to customise your CV and cover letter for this role. Highlight your experience with ML models, statistics, and any relevant work in insurtech or fintech. We love seeing candidates who take the time to connect their skills directly to what we're looking for!
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, so make sure your key achievements and skills stand out without unnecessary fluff.
Apply Through Our Website:Don’t forget to submit your application through our website! It’s the best way for us to keep track of your application and ensures you’re considered for the role. Plus, it shows you’re serious about joining our team at StudySmarter!
How to prepare for a job interview at Safeguest
✨Know Your Data Science Fundamentals
Brush up on your statistics, probability, and machine learning concepts. Be ready to discuss how you've built and deployed ML models in production, as this role is all about turning data into actionable insights.
✨Showcase Your Problem-Solving Skills
Prepare examples of how you've tackled real-world data challenges, especially in fraud detection or risk scoring. Highlight your pragmatic approach to scoping and prioritising tasks, as this will resonate with the company's ownership-driven mindset.
✨Understand the Business Context
Familiarise yourself with Safeguest's platform and the short-term rental market. Being able to connect your technical skills to their business needs will demonstrate your genuine interest and help you stand out.
✨Ask Insightful Questions
Prepare thoughtful questions about the company's risk engine and their vision for the data science role. This shows that you're not just interested in the job, but also in how you can contribute to making their product world-class.
