Adjunct Faculty in Data Science & Analytics

Hult
London
6 months ago
Applications closed

Related Jobs

View all jobs

Artificial Intelligence, Lakenheath, The Undergraduate School - Adjunct Faculty

Artificial Intelligence, Lakenheath, The Undergraduate School - Adjunct Faculty

Careers at Hult

View Open PositionsMore From HultDownload BrochureApply NowGet in Touch

Programs

Bachelor's DegreeMaster’s DegreesMBA DegreesDoctorate DegreesCoaching QualificationsApprenticeships

Locations

Hult BostonHult San FranciscoHult LondonHult DubaiHult New YorkHult SingaporeHult Ashridge

About Hult

AboutAlumniResearchFacultyCareersAccreditations & RankingsHult BlogUpcoming EventsAboutNewsroom AlumniResearchApply

Menu

Careers at Hult

View Open PositionsBack to Jobs

Adjunct Faculty in Data Science & Analytics

London, United Kingdom

Part time - Mid ��� Senior Level

Faculty & Research

The Opportunity

Hult International Business School seeks impactful teachers to train and launch the next generation of international business leaders. Specifically, we are seeking an Adjunct Faculty in Data Science & Analytics with our undergraduate business program in London, UK. The successful candidate has a proven record of facilitating excellent student experiences. Even more important is the candidate’s demonstrated willingness to improve teaching practices and to innovate new tools and techniques to achieve student learning objectives. The ideal candidate will also have active research projects that have and will continue to generate relevant and rigorous intellectual contributions in both academic and practitioner-oriented channels. Finally, the candidate should desire to join our community with an explicit desire to improve all aspects of the Hult educational experience, including those outside of the classroom.

Candidates who are able to teach the following courses will be given priority:

Introduction to Data Analytics Fundamentals of Business Analytics Introduction to Programming with Python Supervised Machine Learning

Expected Qualifications:

Minimum of a Master’s degree related to the area of teaching, with a preference for an earned doctorate (PhD or DBA) from an AACSB-accredited institution. Applicants within a year of completing their doctorate (i.e. ABD) will also be considered Demonstrated knowledge and teaching experience in Data Science & Analytics at a graduate and undergraduate level, with an average teaching evaluation above 4 out of 5. Evidence of successful teaching to large groups of internationally diverse students at the university level. A Hult classroom with 65 students may represent 40 different nationalities. Evidence of scholarly activity directly related to the applicant’s discipline, with at least one peer-reviewed article and one practitioner-related output per year. Professional experience in international business is highly desirable to bring direct, relevant business practice into the classroom. Candidates who have extensive professional experience without a PhD may be considered.

Applications should include a CV and official teaching evaluations. Candidates who progress in the interview process will be asked for three references.

Please note that you must have the right to work in the UK without visa sponsorship, now or in the future, to be considered for this position.

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 AI Tools Do You Need to Know to Get an AI Job?

If you are job hunting in AI right now it can feel like you are drowning in tools. Every week there is a new framework, a new “must-learn” platform or a new productivity app that everyone on LinkedIn seems to be using. The result is predictable: job seekers panic-learn a long list of tools without actually getting better at delivering outcomes. Here is the truth most hiring managers will quietly agree with. They do not hire you because you know 27 tools. They hire you because you can solve a problem, communicate trade-offs, ship something reliable and improve it with feedback. Tools matter, but only in service of outcomes. So how many AI tools do you actually need to know? For most AI job seekers: fewer than you think. You need a tight core toolkit plus a role-specific layer. Everything else is optional. This guide breaks it down clearly, gives you a simple framework to choose what to learn and shows you how to present your toolset on your CV, portfolio and interviews.

What Hiring Managers Look for First in AI Job Applications (UK Guide)

Hiring managers do not start by reading your CV line-by-line. They scan for signals. In AI roles especially, they are looking for proof that you can ship, learn fast, communicate clearly & work safely with data and systems. The best applications make those signals obvious in the first 10–20 seconds. This guide breaks down what hiring managers typically look for first in AI applications in the UK market, how to present it on your CV, LinkedIn & portfolio, and the most common reasons strong candidates get overlooked. Use it as a checklist to tighten your application before you click apply.

The Skills Gap in AI Jobs: What Universities Aren’t Teaching

Artificial intelligence is no longer a future concept. It is already reshaping how businesses operate, how decisions are made, and how entire industries compete. From finance and healthcare to retail, manufacturing, defence, and climate science, AI is embedded in critical systems across the UK economy. Yet despite unprecedented demand for AI talent, employers continue to report severe recruitment challenges. Vacancies remain open for months. Salaries rise year on year. Candidates with impressive academic credentials often fail technical interviews. At the heart of this disconnect lies a growing and uncomfortable truth: Universities are not fully preparing graduates for real-world AI jobs. This article explores the AI skills gap in depth—what is missing from many university programmes, why the gap persists, what employers actually want, and how jobseekers can bridge the divide to build a successful career in artificial intelligence.