At a Glance
- Tasks: Lead AI initiatives and shape the future of Applied Science at ASOS.
- Company: Join ASOS, a global fashion retailer that celebrates individuality and creativity.
- Benefits: Enjoy 25 days annual leave, private medical care, and flexible benefits.
- Other info: Be part of a dynamic community with opportunities for personal and professional growth.
- Why this job: Make a real impact in AI while working with a diverse and innovative team.
- Qualifications: Experience in leading AI projects and strong communication skills required.
The predicted salary is between 70100 - 98000 £ per year.
We're ASOS, the online retailer for fashion lovers all around the world.
We exist to give our customers the confidence to be whoever they want to be, and that goes for our people too.
At ASOS, you're free to be your true self without judgement, and channel your creativity into a platform used by millions.
Everyone needs some help showing up as their best self.
We're Disability Confident Committed - let our Talent team know if you need any reasonable adjustments throughout the recruitment process
Job Description
ASOS is evolving towards an AI-enabled operating model where intelligence, automation and AI-powered workflows increasingly shape how we trade, forecast, personalise experiences and operate at scale.
We see AI not as a standalone capability, but as a foundational part of how we serve customers, make decisions and run our business.
As part of this evolution, we\'re looking for a
Principal Applied Scientist to help shape and scale Applied Science across ASOS.
This role sits at the intersection of
- Applied Science leadership
- AI platform enablement
- Federated operating model governance
- Gen AI-enabled ways of working
Today, Applied Science expertise exists across multiple business domains, including recommendations, forecasting, pricing, consumer modelling and marketing measurement.
While this structure enables close alignment with business needs, it can also create opportunities for greater consistency, collaboration and reuse across teams.
In this role, you\'ll help establish the operating model, standards and technical direction that enable Applied Science teams to deliver impact at scale while maintaining flexibility within their domains.
This is a highly collaborative leadership role that combines technical expertise, systems thinking, stakeholder partnership and the ability to translate strategy into practical outcomes.
- What you’ll be doing
- Applied Science Operating Model Leadership
- Define and evolve the Applied Science operating model across federated business domains.
- Establish shared approaches for model development, evaluation, experimentation, deployment and monitoring.
- Promote the adoption of common tooling, reusable components and MLOps best practices.
- Develop governance frameworks that support consistency while enabling teams to move effectively within their domains.
- Encourage reuse of AI capabilities and platform components to reduce duplication and accelerate delivery.
- AI Platform & Product Enablement
- Partner with Data Platform and Engineering teams to help shape the Applied Science platform roadmap.
- Support the prioritisation and simplification of platform requirements across science teams.
- Promote reusable frameworks, feature stores, evaluation capabilities and deployment standards.
- Foster a strong Applied Science community through technical forums, shared practices and knowledge exchange.
- Support the growth of scientific capability through mentorship, technical review and inclusive hiring practices.
- Work with senior stakeholders across Product, Technology, Commercial and Operations to align priorities and drive business impact.
- Community & Technical Leadership
- Help build and strengthen the Applied Science community across ASOS.
- Establish forums, review practices and knowledge-sharing mechanisms that encourage collaboration and learning.
- Mentor scientists and support the continuous improvement of technical and scientific practices.
- Contribute to hiring processes and help develop consistent approaches to technical assessment.
- Partner with stakeholders across Product, Technology, Commercial and Operations to align on opportunities and priorities.
- Support the adoption of Gen AI, AI-assisted tooling and emerging AI-powered ways of working across the Applied Science lifecycle.
- Explore opportunities to improve experimentation, feature engineering, model evaluation, documentation and development workflows through AI-enabled approaches.
- Identify where AI assistants and autonomous workflows can improve efficiency and productivity.
- Collaborate with Engineering and Product teams to shape AI-native ways of working.
- Strategic AI Leadership
- Contribute to the development of ASOS\'s broader AI strategy and transformation agenda.
- Help translate successful models and experiments into scalable enterprise capabilities.
- Contribute to long-term thinking around agentic commerce, personalisation and AI-enabled operating models.
- Clearly communicate scientific strategy, opportunities and outcomes to senior and executive stakeholders.
Qualifications
We\'re interested in candidates who bring experience in several of the following areas.
We recognise that expertise can be developed through a variety of career paths and experiences, and we encourage applications from people whose backgrounds may differ from those listed here.
Experience
- Experience leading Applied Science, Machine Learning or AI initiatives within large-scale consumer, retail, marketplace or technology environments.
- Experience working within federated or matrixed organisations.
- Experience delivering production machine learning systems at scale.
- Experience developing technical standards, governance approaches and operating models across multiple teams.
- Experience collaborating with platform and engineering teams on ML infrastructure and MLOps capabilities.
- Experience leading cross-functional initiatives involving multiple stakeholders.
- Strong communication skills, with the ability to build alignment and bring people together around shared goals.
- Technical Expertise
Knowledge of, or experience with, several of the following areas
- Forecasting and predictive modelling
- MLOps and machine learning platform design
- Experimentation and evaluation frameworks
- Large Language Models (LLMs), Gen AI and agentic systems
- Modern data and AI ecosystems, including platforms such as Databricks, Azure, feature stores and orchestration frameworks
- Leadership Capabilities
- Ability to influence and build alignment across teams, regardless of reporting structures.
- Strong systems thinking and platform-oriented mindset.
- Ability to balance long-term governance with practical delivery needs.
- Excellent communication and stakeholder engagement skills.
- Coaching, mentoring and capability-building experience.
- Comfortable operating across strategic, organisational and technical contexts.
- Additional Information
- Employee sample sales
- 25 days paid annual leave + an extra celebration day for a special moment
- Discretionary bonus scheme
- Private medical care scheme
- Flexible benefits allowance - which you can choose to take as extra cash, or use towards other benefits
- Opportunity for personalised learning and in-the-moment experiences that enable you to thrive and excel in your role
- #J-18808-Ljbffr
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We think this is how you could land Principal Applied Scientist in London
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We think you need these skills to ace Principal Applied Scientist in London
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!
Craft a Tailored Cover Letter:For a full-time role at ASOS, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at ASOS. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at ASOS
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
✨Showcase Your Projects
Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at ASOS!
✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
