Commercial data analyst

Harnham
City of London
4 months ago
Applications closed

Related Jobs

View all jobs

Commercial Data Analyst

Commercial Data Analyst & National Account Manager (Training Pathway)

Commercial Data Analyst

Commercial Data Analyst

Commercial Data Analyst

Commerical Data Analyst

COMMERCIAL DATA ANALYST

SALARY UP TO £53,700

LONDON – HYBRID 1-2 DAYS A WEEK IN OFFICE


We’re partnering with an established global business seeking a Commercial Data Analyst to take full ownership of the company’s analytics strategy and delivery. This role will work closely with senior stakeholders to drive data-led decision making and foster a more data-literate culture across the organisation.


THE ROLE AND RESPONSIBILITES:

  • Lead the end-to-end analytics strategy and reporting framework.
  • Partner with business leaders to gather and analyse data from multiple sources (Salesforce, NetSuite, Google Analytics).
  • Design and maintain dashboards and BI tools (Power BI, DOMO).
  • Deliver insights and recommendations across pricing, product, and market performance.
  • Automate reporting processes and train stakeholders to use them effectively.
  • Translate complex data into clear insights for non-technical audiences.
  • Support strategic planning, budgeting, and forecasting.
  • Analyse deal-level, product, and geographic trends to identify growth opportunities.
  • Assess marketing ROI, publishing trends, and commercial indicators.
  • Use NPS and customer metrics to evaluate commercial effectiveness.


YOUR SKILLS:

  • Strong commercial acumen with proven experience delivering analytics in a commercial-facing role.
  • Excellent communication skills with the ability to translate complex data into clear, actionable insights for non-technical audiences.
  • Confident stakeholder manager, experienced in engaging with and influencing senior leaders across multiple business areas.
  • Highly self-motivated and proactive, comfortable operating independently in a stand-alone data role.
  • Strong analytical mindset with advanced system and IT skills.
  • Skilled in building SQL queries and using Python for data analysis, aggregation, and automation.
  • Experienced in developing dashboards and reports using BI tools such as Power BI, Tableau, or DOMO.
  • Adept at gathering and interpreting digital and social media data to support commercial decision-making.
  • Familiar with integrating and analysing data via APIs, Salesforce (Sales and Marketing Cloud), and Google Analytics.
  • Proven ability to drive process improvements and deliver measurable business value through data insights.


HOW TO APPLY:

Apply by sending your CV to Joe by the link below

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 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.

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.

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.