Senior Analyst & Data Specialist

Liverpool
9 months ago
Applications closed

Related Jobs

View all jobs

Senior Data Analyst

Data Scientist AI

Data Analyst

Energy & Water Data Analyst

Business Data Analyst

Data Analyst

Senior Analyst & Data Specialist

Location: Liverpool

Working style: Hybrid

About the Role:

In this role, the successful candidate will play a key part in supporting data projects, providing expertise in database development, data security, and documentation. Working closely with the wider data project team, you will help implement a new core platform and support the Finance Domain's reporting requirements. You will interpret raw data, transforming it into actionable insights for senior stakeholders. This role involves daily collaboration with the Data Architect, Head of Data and Analytics, and the Director of Finance, Risk, and Compliance.

Key Responsibilities:

Perform advanced data analysis on large datasets to extract actionable insights.
Identify/interpret trends, patterns and correlations to support strategic and operational decision-making.
Conduct detailed analyses across the business, producing clear, informative outputs and making recommendations that influence key business decisions.
Create clear and concise visualisation's to communicate data insights to both technical and non-technical stakeholders.
Automate reporting processes to enhance efficiency and accuracy.
Collaborate with product, marketing, finance, and operations teams to identify data-driven business opportunities.
Translate business requirements into technical specifications for data extraction and analysis.
Develop methods to ensure data integrity, accuracy, and consistency.
Establish and promote best practices for data management, storage, and security.
Work with IT and Data Engineering teams to optimise data pipelines and infrastructure.

You will need:

Experience in a senior Business Intelligence role, preferably within the finance industry.
Strong SQL skills within a reporting environment.
Proficiency with business reporting software solutions (Power BI preferred).
Detail-oriented approach, with a focus on delivering high-quality, accurate work.
Ability to manage multiple projects and work under tight deadlines when needed.

Desirable Requirements:

Experience with cloud platforms such as AWS, Google Cloud and Azure (or other similar systems)
Knowledge of data governance and compliance regulations (e.g., GDPR).

Additional Information:

The company we are partnered with will not be providing sponsorship for this role.

Inventum Group is passionate about equity, diversity and inclusion. We seek individuals from the widest talent pool and encourage underrepresented talent to apply for vacancies with us. We are committed to recruitment processes that are fair for all, regardless of background and personal characteristics. If you require any adjustments to apply for a role with us, please let us know in whatever way suits you best. Inventum Group is a Recruitment and ED&I Consultancy Business.

Inventum Group is acting as an Employment Agency in relation to this vacancy

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.

Machine Learning Jobs for Career Switchers in Their 30s, 40s & 50s (UK Reality Check)

Are you considering a career change into machine learning in your 30s, 40s or 50s? You’re not alone. In the UK, organisations across industries such as finance, healthcare, retail, government & technology are investing in machine learning to improve decisions, automate processes & unlock new insights. But with all the hype, it can be hard to tell which roles are real job opportunities and which are just buzzwords. This article gives you a practical, UK-focused reality check: which machine learning roles truly exist, what skills employers really hire for, how long retraining realistically takes, how to position your experience and whether age matters in your favour or not. Whether you come from analytics, engineering, operations, research, compliance or business strategy, there is a credible route into machine learning if you approach it strategically.

How to Write a Machine Learning Job Ad That Attracts the Right People

Machine learning now sits at the heart of many UK organisations, powering everything from recommendation engines and fraud detection to forecasting, automation and decision support. As adoption grows, so does demand for skilled machine learning professionals. Yet many employers struggle to attract the right candidates. Machine learning job adverts often generate high volumes of applications, but few applicants have the blend of modelling skill, engineering awareness and real-world experience the role actually requires. Meanwhile, strong machine learning engineers and scientists quietly avoid adverts that feel vague, inflated or confused. In most cases, the issue is not the talent market — it is the job advert itself. Machine learning professionals are analytical, technically rigorous and highly selective. A poorly written job ad signals unclear expectations and low ML maturity. A well-written one signals credibility, focus and a serious approach to applied machine learning. This guide explains how to write a machine learning job ad that attracts the right people, improves applicant quality and strengthens your employer brand.

Maths for Machine Learning Jobs: The Only Topics You Actually Need (& How to Learn Them)

Machine learning job adverts in the UK love vague phrases like “strong maths” or “solid fundamentals”. That can make the whole field feel gatekept especially if you are a career changer or a student who has not touched maths since A level. Here is the practical truth. For most roles on MachineLearningJobs.co.uk such as Machine Learning Engineer, Applied Scientist, Data Scientist, NLP Engineer, Computer Vision Engineer or MLOps Engineer with modelling responsibilities the maths you actually use is concentrated in four areas: Linear algebra essentials (vectors, matrices, projections, PCA intuition) Probability & statistics (uncertainty, metrics, sampling, base rates) Calculus essentials (derivatives, chain rule, gradients, backprop intuition) Basic optimisation (loss functions, gradient descent, regularisation, tuning) If you can do those four things well you can build models, debug training, evaluate properly, explain trade-offs & sound credible in interviews. This guide gives you a clear scope plus a six-week learning plan, portfolio projects & resources so you can learn with momentum rather than drowning in theory.