Principal Data Scientist - Optimisation

easyJet
Luton
9 months ago
Applications closed

Related Jobs

View all jobs

Principal Data Scientist

Principal Data Scientist

Principal Data Scientist and Machine Learning Researcher

Principal Data Engineer (GCP)

Principal Data Analyst

Principal Data Engineer

Principal Data Scientist - Optimisation
Luton/Hybrid

When it comes to innovation and achievement there are few organisations with a better track record. Join us and you’ll be able to play a big part in the success of our highly successful, fast-paced business that opens up Europe so people can exercise their get-up-and-go. With over 320 aircraft flying over 1000 routes to more than 30 countries, we’re the UK’s largest airline, the second largest in Europe and the eighth largest in the world. Flying over 90 million passengers a year, we employ over 14,000 people. Its big-scale stuff and we’re still growing.

JOB PURPOSE
The Principal Data Scientist role is pivotal in transforming the company into a data-driven organization, responsible for supporting the implementation of the vision for the company's data science capabilities and ensuring the alignment of data-related projects with easyJet’s overall objectives.
This role is focused specifically on operational use cases – such as network planning, crew optimisation, maintenance, and disruption management – and the data science solutions delivered are expected to drive efficiency, resilience and cost-effectiveness in operational performance.

Responsibilities include managing and supporting data science resources, providing leadership employing Agile methodologies to deliver data science developments, and actively participating in hiring to build a robust data science team.

The role may involve expertise in either machine learning (e.g., predictive modelling, classification, anomaly detection) or mathematical optimisation (e.g., scheduling, resource allocation, route optimisation). Candidates with a strong background in one area and interest in the other are encouraged to apply.

The role demands leading the development and deployment of data science solutions, managing the entire project lifecycle, and collaborating with the Centre of Enablement (COE) to implement training and best practices.

JOB ACCOUNTABILITIES
Manage data science resource allocation within the team, ensuring optimal use of personnel and tools to meet project demands.
Provide technical support and guidance to data science team members, assisting in troubleshooting and resolving complex issues.
Apply Agile methodologies and a hypothesis-driven approach as required.
Take a lead role in hiring and building a high-performing team of Data Scientists.
Contribute to the recruitment and development of data talent across ITD&C and broader business functions.
Guide and mentor colleagues to support their growth and development.
Collaborate with the Data Management team to improve data quality and increase trust in analytical outputs.
Lead the development and deployment of data science solutions for a specific business area, ensuring alignment with strategic goals.
Lead the end-to-end delivery of data science projects, from gathering and shaping requirements to liaising with key stakeholders to identify and deliver appropriate solutions that meet project objectives.
Provide direction on tools and capabilities to optimise team performance and enhance overall efficiency, ensuring maximum productivity.
Own and define key performance indicators (KPIs) and diagnostics to measure performance against business goals, ensuring precise data-driven decision making.
Collaborate with the Centre of Excellence (COE) to implement training and best practice capabilities effectively and sustainably, enhancing skills across the team.

KEY SKILLS REQUIRED
Experience in coaching, training, or mentoring groups and individuals in both technical and developmental capacities.
Excellent communication skills, with the ability to convey complex analytical concepts to both technical and non-technical stakeholders.
Experience developing training materials in academic or business settings, particularly around data literacy (strongly preferred).
Commercial experience in Data and Analytics.
Proficiency in SQL (any variation).
Experience using Python to extract, manipulate, and summarize data from large databases.
Proven expertise in managing teams that develop and deploy complex descriptive and predictive algorithms.
Working knowledge of statistical techniques (e.g., time series analysis, multivariate analysis, forecasting) and/or mathematical optimisation methods (e.g., linear programming, constraint programming, heuristic approaches).
Experience working in cross-functional teams and projects.
Proven ability to deliver presentations to senior audiences, with the flexibility to adapt communication style as needed.
Demonstrates ownership, responsiveness, and commitment to both individual and team responsibilities.
Experienced in using a wide range of coaching and mentoring skills to support peers and individuals across the broader business.

Desirable Skills and Experience
Prior experience working in Airline/Aviation

What you’ll get in return
Competitive base salary
Up to 30% bonus
25 days holiday
BAYE, SAYE & Performance share schemes
7% pension
Life Insurance
Work Away Scheme
Flexible benefits package
Excellent staff travel benefits

About easyJet

At easyJet our aim is to make low-cost travel easy – connecting people to what they value using Europe’s best airline network, great value fares, and friendly service.

It takes a real team effort to carry over 90 million passengers a year across 35 countries. Whether you’re working as part of our front-line operations or in our corporate functions, you’ll find people that are positive, inclusive, ready to take on a challenge, and that have your back. We call that our ‘Orange Spirit’, and we hope you’ll share that too.

Apply

Complete your application on our careers site.

We encourage individuality, empower our people to seize the initiative, and never stop learning. We see people first and foremost for their performance and potential and we are committed to building a diverse and inclusive organisation that supports the needs of all. As such we will make reasonable adjustments at interview through to employment for our candidates.

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.