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- Tasks: Build intelligent data solutions to empower underwriting decisions and drive business value.
- Company: Join Convex, a data-driven insurance company with a focus on innovation.
- Benefits: Enjoy competitive salary, generous leave, private health cover, and learning budget.
- Why this job: Make a real impact using cutting-edge AI and data modelling in a dynamic team.
- Qualifications: STEM degree, Python expertise, and experience in data science and modelling.
- Other info: Great career growth opportunities in a collaborative, agile environment.
The predicted salary is between 36000 - 60000 £ per year.
In Convex, we are shifting our focus from how we collect data to how we apply it. As a talented Applied Data Scientist, you will move beyond standard analysis to build intelligent solutions that directly empower our underwriting community and beyond to make cleaner underwriting decisions. This is a hands-on, highly technical role that requires a "go-getter" mentality; someone who can identify a business problem, model the solution, and explain the "why" to non-technical stakeholders.
Convex is a data-driven insurance company adopting a culture of small, highly skilled teams building the core intellectual property while all operational needs are met with Software as a Service (SaaS) or outsourced providers. Our 3 strategic pillars are to be our customer’s favourite insurer, to achieve operational excellence and to make better decisions using data and technology. Central to this is the industry-leading usage of Generative AI, LLMs, and advanced data modelling.
We are looking for a tenacious, STEM-educated professional to join our team.
Key Responsibilities- Ensuring we deliver value from data to our business stakeholders, by:
- Taking validated hypotheses that solve real world problems, turning these into production grade solutions.
- Moving beyond reporting to build predictive models and tools that help Underwriters make better, faster decisions.
- Working as a key member of a cross-functional squad, collaborating with engineers and business stakeholders to deliver end-to-end data products.
- Identifying opportunities to transition from simple data collection to advanced data usage that supports the Underwriting community's objectives.
- Acting as a technical leader by training team members on best practices in Python, modelling, and technical execution.
- Working with stakeholders to define, document, prioritise business requirements.
- Ensure business value of the requirements are understood by the squad.
- Communicating with stakeholders on timelines, expected outcomes and providing transparency.
- Technical proficiencies and abilities:
- Deep proficiency in the Python Data Science ecosystem (e.g., Pandas, Scikit-learn, PyTorch/TensorFlow).
- Mastery of SQL is essential; experience with Snowflake is highly preferred.
- Solid experience with AWS in deploying solutions, including Generative AI services (Bedrock / AgentCore etc).
- Practical knowledge of Generative AI, including Large Language Models (LLMs) and agentic workflows.
- Strong understanding of Data Modelling and how to structure data effectively for scalable science projects.
- Must be able to work in a scaled agile environment involving cross functional execution teams.
- Source repository control and devops methodologies eg: github actions workflows.
- Soft Skills:
- Ability to simplify and translate complex data findings to cater to various stakeholders.
- A desire to train and upskill others within the team.
- A proactive mindset with the ability to work autonomously and propose new ideas to the business.
- Must be able to communicate with stakeholders effectively and keep them updated on scope, timelines, and outcomes.
- Direct experience working within the General Insurance or Specialty Insurance markets.
- A deep understanding of the Underwriting lifecycle, including risk selection, pricing adequacy, and exposure management.
- Strong STEM background with a highly numerate degree.
- Candidates with an Actuarial background (either qualified or making significant progress through exams) are highly encouraged to apply.
- The ability to bridge the gap between traditional actuarial science and modern machine learning is a significant advantage.
- Proven track record of building models that have been deployed into a live production environment (not just "sandbox" projects).
- Experience handling messy, disparate insurance datasets and transforming them into structured, high-value inputs for predictive modelling.
- Competitive Salary
- 30 days Annual Leave
- Birthday Leave
- 10% Employer Pension Contribution
- Private Health Insurance Medical Cover
- Group Income Protection
- Life Assurance Cover
- Enhanced Parental Leave
- Annual Health Check
- 3 days of Volunteer Leave each year
- 10 days of help with care (elder/ childcare) through Bright Horizons
- £1,300 to spend on learning & wellbeing
- Give as You Earn
- Cycle to Work
- Season Ticket Loan
Applied Data Scientist in London employer: Convex
Contact Detail:
Convex Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Applied Data Scientist in London
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend meetups, and connect with current employees at Convex. A friendly chat can sometimes lead to opportunities that aren’t even advertised.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your data science projects, especially those involving Python and predictive modelling. This gives you a chance to demonstrate your hands-on experience and problem-solving abilities.
✨Tip Number 3
Prepare for interviews by brushing up on your communication skills. Practice explaining complex data concepts in simple terms, as you'll need to engage with non-technical stakeholders at Convex.
✨Tip Number 4
Don’t forget to 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 the Convex team.
We think you need these skills to ace Applied Data Scientist in London
Some tips for your application 🫡
Show Your Data Skills: Make sure to highlight your technical proficiencies, especially in Python and SQL. We want to see how you've applied these skills in real-world scenarios, so don’t hold back on sharing specific examples!
Tailor Your Application: Take a moment to customise your application for the Applied Data Scientist role. We love seeing candidates who understand our focus on data application and can articulate how their experience aligns with our goals.
Communicate Clearly: Remember, you’ll need to explain complex data findings to non-technical stakeholders. Use your application to demonstrate your ability to simplify and communicate effectively—this is key for us!
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’re considered for this exciting opportunity at Convex!
How to prepare for a job interview at Convex
✨Know Your Data Tools Inside Out
Make sure you’re well-versed in the Python Data Science ecosystem, especially libraries like Pandas and Scikit-learn. Brush up on your SQL skills too, as you'll likely be asked to demonstrate your technical proficiency during the interview.
✨Showcase Your Problem-Solving Skills
Prepare to discuss specific examples where you've identified a business problem and modelled a solution. Convex is looking for someone who can not only build predictive models but also explain the 'why' behind them to non-technical stakeholders.
✨Communicate Clearly and Effectively
Practice simplifying complex data findings into layman's terms. You’ll need to show that you can keep stakeholders updated on timelines and outcomes, so think about how you can convey your ideas clearly and concisely.
✨Demonstrate Your Team Spirit
Convex values collaboration, so be ready to talk about your experience working in cross-functional teams. Highlight any instances where you’ve trained or upskilled others, as this shows your proactive mindset and willingness to contribute to team success.
