Data Analyst Python SQL - Start-up

Client Server
Greater London
7 months ago
Applications closed

Related Jobs

View all jobs

Graduate Data Analyst - Python

Graduate Data Analyst - Python

Graduate Data Analyst - Python

Graduate Data Analyst - Python

Graduate Data Analyst - Python

Graduate Data Analyst - Python

Data Analyst / BA (Python SQL Tableau) London / WFH to £85k

Are you a bright, ambitious, tech savvy Data Analyst?

You could be progressing your career in a Data Analyst / BA role at a scale-up tech company, that enables smart matching for commercial van drivers and consumers, via job bidding and route optimisation, streamlining processes and delivering CO2 carbon neutral targets, the company has been established 10 years, with recent funding of £125 million.
You'll be working on complex and interesting systems and can enjoy a range of benefits and perks.

As a Data Analyst on the Routing Team you will be collaborating with c-suite business stakeholders, data scientists and product leaders to shape and scale the core algorithmic decision-making platform across multiple countries.

You'll be partnering with data scientists and the business to optimise routes and drive efficiencies using data. You'll design, run and analyse A/B tests and simulations to validate hypothesis and recommend improvements based on impact, feasibility and strategy as well as building interactive dashboards to uncover trends, anomalies and opportunities for optimisation and improvement.

Leading deep-dive analysis you'll create compelling narratives around quantitative insights to influence product roadmaps and decision making.

There's an emphasis on getting stuff done and immediate business impact, alongside longer term strategy.

Location / WFH:
There's a hybrid work from home model with three days a week in the Hammersmith office and the other two work from home. When in the office you can enjoy the onsite gym, barista coffee and free breakfast.

About you:

You have achieved a 2.1 or above in a relevant STEM discipline You have strong SQL, Python and dashboarding (e.g. Tableau) skills You have experience of designing, executing and analysing A/B tests or large-scale simulations to deliver business impact You enjoy ambiguous problem solving You're comfortable in a fast paced, start-up environment with changing priorities You have excellent communication, collaboration and stakeholder management skills

What's in it for you:
As a Data Analyst / BA you will receive a competitive package:

Salary to £85k 25 days holiday (increasing to 30), plus Christmas Eve Vitality healthcare plus a host of wellbeing benefits Pension Family leave and enhanced maternity scheme Continual career and self development opportunities Onsite gym, and partnership with Octopus Electric Vehicles and Evans Cycle to Work scheme, there's showers and towels if you do decide to cycle!

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.