At a Glance
- 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.
- Other info: Great career growth opportunities in a collaborative, agile environment.
- 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.
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
At Convex, we pride ourselves on being a forward-thinking employer that champions innovation and collaboration in the heart of London. Our dynamic work culture fosters continuous learning and growth, offering employees the chance to engage with cutting-edge technologies like Generative AI while enjoying a comprehensive benefits package that includes generous annual leave, private health insurance, and support for personal development. Join us to be part of a team that values your contributions and empowers you to make impactful decisions in the insurance industry.
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We think this is how you could land Applied Data Scientist in London
✨Get Involved in Data Science Meetups
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We think you need these skills to ace Applied Data Scientist in London
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
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Craft a Tailored Cover Letter:For a full-time role at Convex, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at Convex. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at Convex
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
✨Showcase Your Projects
Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at Convex!
✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
