Operational Finance Analyst

Chelmsley Wood
4 weeks ago
Create job alert

Key Responsibilities:

• Identify financial status by comparing and analysing actual results with plans and forecasts.

o Liaising with procurement team to compare and contrast budgeting P & Ls vs Actuals at customer and

trade level

• Improve financial status by analysing results; monitoring variances; identifying trends; recommending actions to

management

o Review intermonth variances and review costs against company procedures e.g. profit expectations and

director sign off

o Review open reserves to identify trends and compare actual invoices v open reserves

• Guide cost analysis process by establishing and enforcing policies and procedures; providing trends and

forecasts; explaining processes and techniques; recommending actions

o Enforce company policies and procedures and offer training to departments and team members to

ensure costing improvement

• Recommend actions by analysing and interpreting data and making comparative analyses; studying proposed

changes in methods and materials.

o Liaise with all members of the business to suggest best practice and make efficiencies to costing process

• Increase productivity by developing automated accounting applications; coordinating information requirements

• Support operational and commercial departments where required, such as:

o Making them aware of any changes/additions needed to SOP’s

o Ensuring you are comfortable with roll out of any new tasks, helping to ensure these go smoothly

o Helping with writing new process SOP’s

o Helping with data integrity checks when required, weekly, monthly or quarterly

o Ensuring rates are up to date in SharePoint

General Skills

• Experience in the logistics sector is preferred

• Excellent written and verbal communication skills

• Excellent planning and attention to detail

• Enthusiastic, flexible, and self-motivated

• Excellent usage of computer / operating system (Microsoft Windows) along with other office applications

(Microsoft Office), specifically excel

• May be required to work overtime, weekends and/or be flexible in start/finish times

Related Jobs

View all jobs

Finance Analyst

Strategic Finance & Insights Analyst

FP&A Analyst

Senior Data Analyst

Data & Insights Analyst

Senior Analyst & Data Specialist

Get the latest insights and jobs direct. Sign up for our newsletter.

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

Industry Insights

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

Portfolio Projects That Get You Hired for Data Science Jobs (With Real GitHub Examples)

Data science is at the forefront of innovation, enabling organisations to turn vast amounts of data into actionable insights. Whether it’s building predictive models, performing exploratory analyses, or designing end-to-end machine learning solutions, data scientists are in high demand across every sector. But how can you stand out in a crowded job market? Alongside a solid CV, a well-curated data science portfolio often makes the difference between getting an interview and getting overlooked. In this comprehensive guide, we’ll explore: Why a data science portfolio is essential for job seekers. Selecting projects that align with your target data science roles. Real GitHub examples showcasing best practices. Actionable project ideas you can build right now. Best ways to present your projects and ensure recruiters can find them easily. By the end, you’ll be equipped to craft a compelling portfolio that proves your skills in a tangible way. And when you’re ready for your next career move, remember to upload your CV on DataScience-Jobs.co.uk so that your newly showcased work can be discovered by employers looking for exactly what you have to offer.

Data Science Job Interview Warm‑Up: 30 Real Coding & System‑Design Questions

Data science has become one of the most sought‑after fields in technology, leveraging mathematics, statistics, machine learning, and programming to derive valuable insights from data. Organisations across every sector—finance, healthcare, retail, government—rely on data scientists to build predictive models, understand patterns, and shape strategy with data‑driven decisions. If you’re gearing up for a data science interview, expect a well‑rounded evaluation. Beyond statistics and algorithms, many roles also require data wrangling, visualisation, software engineering, and communication skills. Interviewers want to see if you can slice and dice messy datasets, design experiments, and scale ML models to production. In this guide, we’ll explore 30 real coding & system‑design questions commonly posed in data science interviews. You’ll find challenges ranging from algorithmic coding and statistical puzzle‑solving to the architectural side of building data science platforms in real‑world settings. By practising with these questions, you’ll gain the confidence and clarity needed to stand out among competitive candidates. And if you’re actively seeking data science opportunities in the UK, be sure to visit www.datascience-jobs.co.uk. It’s a comprehensive hub featuring junior, mid‑level, and senior data science vacancies—spanning start‑ups to FTSE 100 companies. Let’s dive into what you need to know.

Negotiating Your Data Science Job Offer: Equity, Bonuses & Perks Explained

Data science has rapidly evolved from a niche specialty to a cornerstone of strategic decision-making in virtually every industry—from finance and healthcare to retail, entertainment, and AI research. As a mid‑senior data scientist, you’re not just running predictive models or generating dashboards; you’re shaping business strategy, product innovation, and customer experiences. This level of influence is why employers are increasingly offering compensation packages that go beyond a baseline salary. Yet, many professionals still tend to focus almost exclusively on base pay when negotiating a new role. This can be a costly oversight. Companies vying for data science talent—especially in the UK, where demand often outstrips supply—routinely offer equity, bonuses, flexible work options, and professional development funds in addition to salary. Recognising these opportunities and effectively negotiating them can have a substantial impact on your total earnings and long-term career satisfaction. This guide explores every facet of negotiating a data science job offer—from understanding equity structures and bonus schemes to weighing crucial perks like remote work and ongoing skill development. By the end, you’ll be well-equipped to secure a holistic package aligned with your market value, your life goals, and the tremendous impact you bring to any organisation.