Time Type:
Full timeWorking Pattern:
HybridPurpose of the RoleWorking closely with members of the Data Science and Analytics team, the holder will contribute across the full model lifecycle, from data preparation and development through to deployment and monitoring, taking on progressively more ownership of projects. Production experience is welcome but not required; what matters is a genuine interest in how models get into operational use and stay healthy once they are there.
The holder will also spend time with commercial underwriters and Actuarial, learning how each class operates and how data science creates value for them.
The position is suitable for someone who shows curiosity about the business and a willingness to learn it's component parts.
Technically, we are looking for solid Python, sound ML and statistical foundations, and good data analysis skills. Familiarity with software engineering practice, cloud ML (Azure in our case) and MLOps is a plus, and the team will invest in developing these through pairing, code review and hands-on project work.
Duties and Accountabilities
Delivery of data science products
- Deliver well-scoped data science projects, from data preparation and exploratory analysis through model development and validation. Working with team to deliver to production.
- Take on increasing ownership of the full lifecycle over time, including deployment and in-life monitoring of models you have built.
- Apply data science techniques to automate manual processes.
- Explore generative AI use cases, helping assess their value and fitness for the roadmap.
- Assess the quality and usefulness of internal and external data sources, working with data scientists and actuaries.
- Produce ad hoc analytics and insights for the business, coordinated through the Data Science and Data Analytics Manager.
Engineering standards
- Write clean, tested, version-controlled Python that follows team standards, and take part in code review as both author and reviewer.
- Contribute to machine learning pipelines in our cloud environment (Azure), learning the team's MLOps and CI/CD patterns through hands-on work.
- Monitor the performance of deployed products, engaging with business to track and measure adoption.
- Document models clearly and explain them to a standard appropriate for a regulated environment.
Stakeholder engagement and requirements
- Spend time with underwriting teams to understand how each class operates and where data science can add value.
- Help capture and document requirements for data science products.
- Work with IT and Data Engineering on the data that projects depend on.
- Communicate findings, and their limitations, clearly to non-technical audiences.
Skills, Knowledge and Experience
Essential
- Solid Python for data science, with readable, well-structured code and an understanding of functions, classes and modules.
- Machine learning across the standard toolkit (scikit-learn, pandas, NumPy, statsmodels or equivalents), with sound judgement about validation and the limits of what the data supports.
- Statistical foundations: hypothesis testing, regression, quantifying uncertainty and recognising when a conclusion is not supported.
- SQL, with the ability to query and join relational data efficiently.
- Data wrangling and preprocessing, including ambiguous, incomplete and inconsistent data.
- Working knowledge of Git and version control.
- Clear written and verbal communication, including explaining technical work to non-technical colleagues.
Desirable
- Evidence of linking analytical work to a business outcome, such as a cost saved, a decision changed or a process automated.
- Experience working directly with business stakeholders on requirements or adoption.
- Insurance experience, ideally in the Lloyd's market, pricing or underwriting, or exposure to actuarial methods.
- Object-oriented design, automated testing (eg. pytest) and code review as normal practice.
- Experience taking a model into production, or contributing to one that was.
- Exposure to MLOps and CI/CD concepts: pipeline orchestration, model versioning, automated deployment and monitoring.
- Dependency and environment management (eg. virtual environments, Docker).
- Distributed processing frameworks such as PySpark.
- Data visualisation and reporting, for example Power BI.
Behaviours and ways of working
- Curiosity about the business: asks why a problem matters and who will use the answer, not only how to model it.
- Growth mindset: seeks out feedback, acts on code review, and takes ownership of their own development.
- Pragmatism: recognises when a simpler solution is good enough and avoids over-engineering.
- Clear communication: explains results, limitations and uncertainty honestly to technical and non-technical audiences.
- Collaboration: works well across Underwriting, Actuarial, IT and Data Engineering, and shares what they learn.
- Reliability: manages their own workload, flags risks early and delivers on commitments.
We make decisions considering the best interests of key stakeholders. We are direct and straightforward in our actions, working collaboratively to create a culture of fairness and respect.
Open and inclusiveWe act with integrity, valuing diversity of thought and background. We take time to listen to the needs of our customers, stakeholders and colleagues working together to seek and share information.
AmbitiousWe have a passion for success, aspiring to be recognised as best in class. We embrace new opportunities, encouraging innovation in pursuit of our goals.
Striving to be betterWe strive to improve at all times, challenging complacency, being agile and adapting to change. We always seek to improve our customers’ experience with us.
Investing in people’s potentialWe provide an environment where each employee can reach their personal potential. We encourage personal accountability for performance and individual ownership for growth and success.
AEGIS London is an equal opportunities employer and recognises the value of a diverse workforce in facilitating better decision making and business growth. We encourage a variety of differing views, perspectives and insights to create a collaborative working environment. Diversity and Inclusion are fundamental to our business and we encourage applications from all backgrounds recognising the diversity of society and our customers.
It’s important to us that you are able to perform at your best when applying for a role with AEGIS London. If there are any adjustments we can reasonably make to ensure that the process is accessible for you please telephone us on +44(0)20 7856 7856 or email [email protected]
As a business, we understand individual circumstances may differ and aim to be adaptable and to support flexible working practices. Talk to our recruitment team to understand how AEGIS London can help support you in reaching your full potential
Skills Required
- Solid Python for data science, including readable, structured code and understanding of functions, classes, and modules
- Machine learning knowledge using tools such as scikit-learn, pandas, NumPy, and statsmodels or equivalents
- Statistical foundations including hypothesis testing, regression, uncertainty quantification, and statistical judgment
- SQL skills for efficiently querying and joining relational data
- Data wrangling and preprocessing of ambiguous, incomplete, and inconsistent data
- Working knowledge of Git and version control
- Clear written and verbal communication with technical and non-technical colleagues
- Evidence of connecting analytical work to a business outcome
- Experience working with business stakeholders on requirements or adoption
- Insurance, Lloyd's market, pricing, underwriting, or actuarial experience
- Object-oriented design, automated testing, and code review experience
- Experience taking a model into production
- Exposure to MLOps and CI/CD concepts
- Dependency and environment management, such as virtual environments or Docker
- Experience with distributed processing frameworks such as PySpark
- Data visualization and reporting experience, such as Power BI
What We Do
Your risks, our resourcefulness. In an increasingly complex and volatile world, brokers and clients are looking for risk transfer partners who are accessible, resourceful and there for the long haul. At AEGIS London, our structure and our mindset mean that our resources are an excellent match for your risks. We’re here to find solutions – our underwriters are available and empowered to try their very hardest to find a way to say yes. We’re here for your future – the fact that we are privately-owned means that we take long term decisions about the business we do. We’re here for you – collaboration is a cornerstone of our culture, our policies and our claims handling. We believe in operating for the mutual benefit of all our business partners. Today, AEGIS London writes a diversified book of business through Syndicate 1225 at Lloyd’s where, year after year, we’re ranked in the top quartile for performance. Our underwriters are recognised as leaders in many of the classes in which we operate.
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