National AI Awards 2025Discover AI's trailblazers! Join us to celebrate innovation and nominate industry leaders.

Nominate & Attend

Data Scientist

Aquent
uk
11 months ago
Create job alert

Overview

Placement Type:

Temporary

Compensation:

£361-£400 per day(PAYE Inside IR35)

Start Date:

Asap

Data Scientist, Analytics Duties

Data Scientist (Analytics) is to help teams make better data-driven decisions. This is done in the following way:

Collect, organize, interpret, and summarize statistical data in order to contribute to the design and development of products Apply your expertise in quantitative analysis, data mining, and the presentation of data to see beyond the numbers and understand how our users interact with both our consumer and business products Partner with Product and Engineering teams to solve problems and identify trends and opportunities Inform, influence, support, and execute our product decisions and product launches May be assigned projects in various areas including, but not limited to, product operations, exploratory analysis, product influence, and data infrastructure Work on problems of diverse scope where analysis of data requires evaluation of identifiable factors Demonstrate good judgment in selecting methods and techniques for obtaining solutions Perform data analyses on tactical (feature-level) and strategic (team objectives and goals) work to drive team direction Develop strategic narrative based on analytical insights and priorities Think about key questions and metrics to define success for any product/feature.In connection with these duties, may apply knowledge of the following:Performing quantitative analysis including data mining on highly complex data sets Data querying languages, such as SQL, scripting languages, such as Python, or statistical or mathematical software, such as R, SAS, or Matlab Applied statistics or experimentation, such as A/B testing, in an industry setting Communicating the results of analyses to product or leadership teams to influence strategy Machine learning techniques ETL (Extract, Transform, Load) processes Relational databases Large-scale data processing infrastructures using distributed systems Quantitative analysis techniques, including clustering, regression, pattern recognition, or descriptive and inferential statistics.

THE ROLE

We have 3 areas in Experiences: Organic (focusing on consumers), Paid (focusing on business/advertisers), Platform (infra to help scale the other two)

We are looking for a Data Scientist to join our Paid Experiences team. Specifically, this will work with our Engineers, Designers and Product Managers to: Understand what integrity experiences prevent advertisers from running ads successfully Help advertisers (self-)remediate to unblock their campaigns, while protecting the organisation from harm

WHO WE ARE LOOKING FOR

Excited about giving millions of users a day a more supportive integrity experience when they face enforcements or encounter harm on the platform Excited about optimising systems for scale at the intersection of user facing experiences and platform capabilities Enjoys thinking through how we best form partnerships with other teams and how scalable solutions should be governed effectively. Enjoys getting their hands dirty to understand data and system disconnects and can drive insightful root-cause-analysis

Minimum Qualifications

Requires a Master’s degree in Computer Science, Engineering, Information Systems, Analytics, Mathematics, Economics, Physics, Applied Sciences, or a related field. Requires knowledge or experience in the following: Performing quantitative analysis including data mining on highly complex data sets. Data querying language: SQL Scripting language: Python Statistical or mathematical software including one of the following: R, SAS, or Matlab Applied statistics or experimentation, such as A/B testing, in an industry setting Machine learning techniques Quantitative analysis techniques, including one of the following: clustering, regression, pattern recognition, or descriptive and inferential statistics

Related Jobs

View all jobs

Data Scientist

Data Scientist - AI / ML, Python, Scripting, Cyber Security

Data Scientist - Inside IR35 contract

Data Scientist

Data Scientist - Commodities

Data Scientist

National AI Awards 2025

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.

LinkedIn Profile Checklist for AI Jobs: 10 Tweaks That Triple Recruiter Views

In today’s fiercely competitive AI job market, simply having a LinkedIn profile isn’t enough. Recruiters and hiring managers routinely scout for top talent in machine learning, data science, natural language processing, computer vision and beyond—sometimes before roles are even posted. With hundreds of applicants vying for each role, you need a profile that’s optimised for search, speaks directly to AI-specific skills, and showcases measurable impact. By following this step-by-step LinkedIn for AI jobs checklist, you’ll make ten strategic tweaks that can triple recruiter views and position you as a leading AI professional. Whether you’re a fresh graduate aiming for your first AI position or a seasoned expert targeting a senior role, these actionable changes will ensure your profile stands out in feeds, search results and recruiter queues. Let’s dive in.

Part-Time Study Routes That Lead to AI Jobs: Evening Courses, Bootcamps & Online Masters

Artificial intelligence (AI) is reshaping industries at an unprecedented pace. From automating mundane tasks in finance to driving innovation in healthcare diagnostics, the demand for AI-skilled professionals is skyrocketing. In the United Kingdom alone, AI is forecast to deliver over £400 billion to the economy by 2030 and generate millions of new jobs across sectors. Yet, for many ambitious professionals, taking time away from work to upskill can feel like an impossible ask. Thankfully, part-time learning options have proliferated: evening courses, intensive bootcamps and flexible online master’s programmes empower you to learn AI while working. This comprehensive guide explores every route—from short tasters to deep-dive MScs—showcasing providers, course formats, funding options and practical tips. Whether you’re a career changer, a busy manager or a self-taught developer keen to go further, you’ll discover a pathway to fit your schedule, budget and goals.