Mid-Level Data Scientists Needed |Financial Services | Guildford Area

Guildford
10 months ago
Applications closed

Related Jobs

View all jobs

Data Engineer

Data Engineer

Junior - Mid Level SQL Server DBA

Data Engineer

Data Engineer (18 Months FTC)

Data Engineer

Mid-Level Data Scientists Needed |Financial Services | Guildford Area

Are you a passionate data scientist with a knack for engineering solutions? Our established financial services client is seeking a talented Mid-Level Data Scientist to join their growing Analytics team at their office near Guildford.

About the Role:

Working in a Data Science role you will also perform some Data Engineering and Analysis tasks. You'll help transform complex financial data into actionable insights that drive business decisions. You'll collaborate with cross-functional teams to develop predictive models using a range of Data Science techniques. They are also planning to implement some Generative AI tools that optimize internal operations. They are still early in their Data Science journey and this will be area they are investing over the next few years so need people who can help shame their Data and AI tools.

Responsibilities:

  • Design, develop and implement predictive models and machine learning algorithms including building Gen-AI tools.

  • Build and maintain data pipelines to support analytical workflows

  • Transform raw financial data into structured formats suitable for analysis

  • Create visualizations and reports to communicate findings to stakeholders

  • Collaborate with business teams to understand requirements and deliver solutions

  • Optimize existing models and processes for improved performance

    Requirements:

  • 3+ years of experience in data science using a range of predictive modelling and Machine Learning techniques

  • Strong programming skills in Python and SQL

  • Experience with data engineering concepts and tools (ETL pipelines, data warehousing – they are using SnowFlake)

  • Knowledge of machine learning libraries and frameworks (e.g., scikit-learn, TensorFlow)

  • Bachelor's degree in Computer Science, Statistics, Mathematics, or related field

    Technical Skills:

  • Data manipulation: Pandas, NumPy

  • Data engineering: Snowflake, Apache Spark, Airflow or similar

  • Database management: SQL, NoSQL databases

  • Visualization: Power BI, Tableau, or equivalent

  • Version control: Git

    Salary: £45,000 - £65,000 DOE + good pension contribution + private medical + 25 days holiday + discretionary bonus

    Join their team and help shape business success through data-driven decision making.

    Location: Guildford area, Surrey Work Model: Hybrid (3 days in office, 2 days remote)

    APPLY TODAY for immediate consideration and interview in the next week

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 Engineering Tools Do You Need to Know to Get a Data Engineering Job?

If you’re aiming for a career in data engineering, it can feel like you’re staring at a never-ending list of tools and technologies — SQL, Python, Spark, Kafka, Airflow, dbt, Snowflake, Redshift, Terraform, Kubernetes, and the list goes on. Scroll job boards and LinkedIn, and it’s easy to conclude that unless you have experience with every modern tool in the data stack, you won’t even get a callback. Here’s the honest truth most data engineering hiring managers will quietly agree with: 👉 They don’t hire you because you know every tool — they hire you because you can solve real data problems with the tools you know. Tools matter. But only in service of outcomes. Jobs are won by candidates who know why a technology is used, when to use it, and how to explain their decisions. So how many data engineering tools do you actually need to know to get a job? For most job seekers, the answer is far fewer than you think — but you do need them in the right combination and order. This article breaks down what employers really expect, which tools are core, which are role-specific, and how to focus your learning so you look capable and employable rather than overwhelmed.

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

If you’re applying for data engineering jobs in the UK, the first thing to understand is this: Hiring managers don’t read every word of your CV. They scan it. They look for signals of relevance, credibility, delivery and collaboration — and if they don’t see the right signals quickly, your application may never get a second look. In data engineering, hiring managers are especially focused on whether you can build and operate reliable, scalable data systems, handle real-world data challenges and work effectively with analytics, BI, data science and engineering teams. This guide breaks down exactly what they look at first in your application — and how to shape your CV, portfolio and cover letter so you stand out.

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

Data engineering has quietly become one of the most critical roles in the modern technology stack. While data science and AI often receive the spotlight, data engineers are the professionals who design, build and maintain the systems that make data usable at scale. Across the UK, demand for data engineers continues to rise. Organisations in finance, retail, healthcare, government, media and technology all report difficulty hiring candidates with the right skills. Salaries remain strong, and experienced professionals are in short supply. Yet despite this demand, many graduates with degrees in computer science, data science or related disciplines struggle to secure data engineering roles. The reason is not academic ability. It is a persistent skills gap between university education and real-world data engineering work. This article explores that gap in depth: what universities teach well, what they consistently miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data engineering.