Applied Data Scientist
Permanent - Full TimeJob Overview
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.
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.
Skills Knowledge and Expertise
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.
Preferred experience:
- 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.