Senior Underwriter

Manchester
1 year ago
Applications closed

Related Jobs

View all jobs

Actuarial Data Scientist

Claims Data Strategy Lead

Senior Data Engineer - (Python & SQL)

Senior Data Engineer

Senior Data Engineer (AWS, Airflow, Python)

Senior Data Engineer

Senior Underwriter

This is a flexible, hybrid opportunity.

Role Purpose:

To support the Product Head in developing and maintaining the range of products to ensure the agreed business plan targets and objectives are met.  Exploiting new product opportunities and providing Product and Technical support both internally and externally.

Key Responsibilities:

Collation and presentation of key information to the Product Head on schedule and ad hoc basis in respect of the product range.

To manage, maintain and review the product range to ensure they remain current, competitive, compliant and able to meet business targets in respect of allocated accounts  and/or schemes.

To present pricing recommendations to the Product Head based on analysis of data, broker intel, pricing and MI team.

To manage new product developments and amendments.

Collation and presentation of key information to the Product Head on schedule and on an ad- hoc basis in respect of the product range.

Fully understand underwriting performance metrics to enable collation and presentation of key account information to the Product Head at agreed intervals and dealing with ad-hoc requests relating to products, pricing and MI.

To research the market, through broker Intel, media and personal contacts, so as to remain up to date on all developing issues and opportunities.

Management of key accounts and other agreed key business relationships, to maintain and develop profitable accounts, and to explore any product opportunities and resolve issues that exist.

Nurture broker relationships to meet the needs of the business plan whilst using that relationship to obtain feedback from our customers of their experience.

To investigate and respond to queries from the operational area in relation to policy issues.

About You:

Experienced Underwriter.

Data Mining Experience.

Has an analytical background.

Previous experience within the insurance market.

Commercial awareness, market knowledge, process awareness.

Data analysis including presentation of observations and proposed actions.

Work well within a team environment as well as able to self-motivate and use initiative.

Good knowledge of Excel functions.

Excellent communication skills.

Knowledge of SAS software would be beneficial.

Why us?

Markerstudy Insurance Services Limited (MISL) is one of the largest Managing General Agents in the UK. With a strong presence in the UK motor insurance market, we specialise in niche motor cover, where our solid market knowledge and experience enables us to create highly targeted products.

Our success is underpinned by our underwriting strategy to identify and apply special risk factors to the customers’ advantage. That, and our skilled underwriting technicians who are friendly, accessible and empowered to make decisions.

We only transact business through professional UK insurance intermediaries and we take pride in fostering excellent working relationships. Our products feature prominently on Aggregators' sites, such as (url removed), Go Compare and Compare the Market, via our broker partners.

What we offer in return?

A collaborative and fast paced work environment

25 days annual leave plus of Bank Holidays and the ability to buy an additional five days holiday

Health Cash Plan

A benefit scheme that offers discounts and cashback on shopping, restaurants, travel and more

Life Assurance 4x annual salary

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.

Careers

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.

Jobs

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.

Education

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.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.