Data Scientist

Gener8
London
1 month ago
Applications closed

Related Jobs

View all jobs

Data Scientist

Data Scientist

Data Scientist

Data Scientist

Data Scientist (Government)

Data Scientist - Renewable Energy

We're looking for an experienced Data Scientist to help us on an exciting new project based on our proprietary clickstream data. We collect tens of millions of events per month, from tens of thousands of users across the world.


About us

Since its launch in 2018, Gener8 has been at the forefront of the “open data” movement: the belief that people should be able to control and be rewarded from their own data. Gener8’s consumer products include a web browser, browser extension, IOS and Android apps. Our products enable people to transparently and willingly share their data with Gener8, whilst preserving their privacy, so that we can create value from it for them.


We are growing fast. With tens of thousands new app downloads every month. Every month our desktop browser racks up the equivalent of 250 yrs in time spent browsing on it. As you can imagine, we have huge amounts of proprietary data which we can create value from.


Gener8 was named ‘Disruptor of the year’ in 2022 by the Great British Entrepreneur Awards. Our investors include 3 Dragons fromas well as personalities such as the rap star Tinie Tempah, former football manager Harry Redknapp and cricketer Chris Gayle to name a few. In 2023 we met with the Prime Minister at Downing street and were invited to become a member of the Government’s new “Smart Data Council”, shaping the future of data legislation in the UK. We also regularly engage with European legislators on the Digital Markets Act which empowers users to control and earn from their data.


The first part of the project will involve modelling our raw clickstream data to make it nationally representative of the UK and US, where we already have relevant national census data.


The second part will be analysing this dataset for behavioural changes amongst several cohorts of users, to measure how it has changed over time (~1yr) and understand this in greater detail.


There is also the opportunity to include our other datasets in this analysis, such as in-app usage.


As well as executing the modelling and analysis we'll be looking to learn from you what the best approach is and questions to ask are, as we discover more through the project.


The final output will firstly be a presentation given to senior business stakeholders, technical experts and other data scientists as well as an accompanying written report.


Relevant skills & experience:

  • Nat Rep modelling
  • Analysing large clickstream (pageview) datasets
  • Python and or R
  • SQL

Technical details

  • Primary dataset: ~200GB, ~360m rows, ~30 columns
  • Dialect: BigQuery - we can provide experienced in-house technical support with complex queries


#J-18808-Ljbffr

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.