Data Business Analyst - Risk Rating & Pricing

London
11 months ago
Applications closed

Related Jobs

View all jobs

Business / Data Analyst

Business Data Analyst

Business Intelligence Analyst

Business Data Analyst

Business Data Analyst

Business Data Analyst

My client is based in the London area and are currently looking to recruit for an experienced Data/Business Analyst to join their team. They are one of the leaders within the consulting sector, and are currently going through a period of growth and are looking for an experienced BI professional to join their team. They have a vision to continually improve and incrementally adapt to their environments.

Your role will include:

Work closely with key business teams to gather and document requirements relating to risk assessment, pricing data, and associated tools and processes.

Carry out analysis on large and complex datasets to support the design, refinement, and monitoring of pricing models.

Assist with the identification, mapping, and analysis of key data sources and the flow of information between systems.

Help develop materials such as data dictionaries, process maps, and system documentation to promote clarity and consistency in how data is used across the organisation.

Facilitate and document workshops with teams including Underwriting, Actuarial, and Technology to capture business input and define technical requirements.

Collaborate with data engineering teams to support data sourcing, preparation, and quality assurance activities.

Produce reports and dashboards (Power BI) to present insights and inform business decision-making.

Contribute to testing and validation activities for pricing tools, ensuring business needs and data requirements are accurately captured.

Act as a link between Underwriting teams and Technology teams, translating business needs into actionable deliverables.

Support data governance initiatives by contributing to data quality improvement efforts and maintaining documentation standards.

My client is providing access to;

Hybrid 2/3 days,
25 Days Holiday, Plus Bank Holiday
Bonus Scheme
And More...

For this role, they are looking for a candidate that has experience in…

Practical understanding of the London Insurance Market landscape along with exposure to pricing platforms such as Radar, HX, or Verisk Rulebook is essential.

Familiarity with concepts surrounding risk assessment, pricing methodologies, or actuarial workflows would be considered advantageous.

Demonstrated background working in roles such as Data Analyst, Business Analyst, or similar analytical positions.

Strong capability in documenting business needs, creating clear data definitions, and mapping out system-related processes. Experience using tools like Oracle SQL Developer and Microsoft Visio is a plus.

Hands-on experience working with relational database systems, including but not limited to SQL Server, Oracle, MySQL, or PostgreSQL.

This role is an urgent requirement, there are limited interview slots left, if interested send an up to date CV to Shoaib Khan - (url removed) or call (phone number removed) for a catch up in complete confidence.

Frank Group's Data Teams offer more opportunities across the UK than any other recruiter We're the proud sponsor and supporter of SQLBits, AWS RE:Invent, Power Platform World Tour, the London Power BI User Group, Newcastle Power BI User Group and Newcastle Data Platform and Cloud User Group

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

How Many Data Science Tools Do You Need to Know to Get a Data Science Job?

If you’re trying to break into data science — or progress your career — it can feel like you are drowning in names: Python, R, TensorFlow, PyTorch, SQL, Spark, AWS, Scikit-learn, Jupyter, Tableau, Power BI…the list just keeps going. With every job advert listing a different combination of tools, many applicants fall into a trap: they try to learn everything. The result? Long tool lists that sound impressive — but little depth to back them up. Here’s the straight-talk version most hiring managers won’t explicitly tell you: 👉 You don’t need to know every data science tool to get hired. 👉 You need to know the right ones — deeply — and know how to use them to solve real problems. Tools matter, but only in service of outcomes. So how many data science tools do you actually need to know to get a job? For most job seekers, the answer is not “27” — it’s more like 8–12, thoughtfully chosen and well understood. This guide explains what employers really value, which tools are core, which are role-specific, and how to focus your toolbox so your CV and interviews shine.

What Hiring Managers Look for First in Data Science Job Applications (UK Guide)

If you’re applying for data science roles in the UK, it’s crucial to understand what hiring managers focus on before they dive into your full CV. In competitive markets, recruiters and hiring managers often make their first decisions in the first 10–20 seconds of scanning an application — and in data science, there are specific signals they look for first. Data science isn’t just about coding or statistics — it’s about producing insights, shipping models, collaborating with teams, and solving real business problems. This guide helps you understand exactly what hiring managers look for first in data science applications — and how to structure your CV, portfolio and cover letter so you leap to the top of the shortlist.

The Skills Gap in Data Science Jobs: What Universities Aren’t Teaching

Data science has become one of the most visible and sought-after careers in the UK technology market. From financial services and retail to healthcare, media, government and sport, organisations increasingly rely on data scientists to extract insight, guide decisions and build predictive models. Universities have responded quickly. Degrees in data science, analytics and artificial intelligence have expanded rapidly, and many computer science courses now include data-focused pathways. And yet, despite the volume of graduates entering the market, employers across the UK consistently report the same problem: Many data science candidates are not job-ready. Vacancies remain open. Hiring processes drag on. Candidates with impressive academic backgrounds fail interviews or struggle once hired. The issue is not intelligence or effort. It is a persistent skills gap between university education and real-world data science roles. This article explores that gap in depth: what universities teach well, what they often miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data science.