Data Analyst

Office Angels
Dartford
2 months ago
Applications closed

Related Jobs

View all jobs

Data Analyst

Data Analyst

Data Analyst

Data Analyst

Data Analyst

Data Analyst

Data Analyst


£31,000- £35,000 per annum


Dartford


Monday-Friday 9am-5pm, Hybrid Working


25 days holiday + bank holiday, closing office over Christmas, parking on site, pension scheme


I'm thrilled to be working with this amazing company to find a Data Analyst to join their amazing team!


Required Skills and Qualifications:


  • Higher education in STEM subjects or equivalent.
  • Excellent numerical and Microsoft Office 365 skills, especially Excel.
  • Experience with data visualisation tools (Power BI or Tableau).
  • Ability to analyse and manipulate large data sets.
  • Understanding of supply chain processes.
  • Familiarity with forecasting models.
  • Ability to travel nationally and internationally.
  • Proficiency in DAX and Power Query.


Key Responsibilities:


  • Develop and utilise financial systems and process controls to support business objectives.
  • Analyse order books for planning and forecasting.
  • Contribute to the demand planning process.
  • Report on sales performance, price, and margin gaps regularly.
  • Provide accurate forecasts and outlooks.
  • Assist in managing rebates and promotional plans.
  • Prepare market insights based on robust research.
  • Support sales team bonus targeting and measurement.
  • Assist R&D, product management, and finance with pricing and margin analysis.
  • Update business reporting tools such as BI.
  • Provide sales pricing and volume data for financial forecasts.
  • Contribute to budget and strategic planning.
  • Manipulate and interpret data from CRM systems.
  • Support sales analysis for UK, EU, and Ireland managers.
  • Assist with specific project initiatives as needed.


Performance Indicators:


  • Timely and accurate forecasting and planning inputs.
  • Price intelligence by customer, range, and market.
  • Volume and margin analysis.
  • KPI dashboards and order book analysis.
  • Actionable analysis and recommendations.
  • Market mapping data.
  • Compliance with internal audit standards.


Job Dimensions:


  • General commercial analysis: 25%
  • Market insights: 25%
  • Bespoke analysis: 10%
  • Demand planning: 15%
  • Financial planning/forecasts: 25%


How to Apply:


  • If you have a knack for numbers and thrive in a fast-paced finance environment, we want to hear from you. Apply now to join our client's team as a Data Analyst!

#J-18808-Ljbffr

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.