Senior Data Scientist & ML Engineer (f/m/d)

awin
London
1 month ago
Applications closed

Related Jobs

View all jobs

Senior Data Scientist - Payments

Senior Data Scientist - Net Revenue & Revenue Growth Management

Senior Data Scientist

Senior Data Scientist

Naimuri - Senior Data Scientist

Senior Data Scientist Research Engineer

Purpose of position

As a Senior Data Engineer, you will play a pivotal role in our AI/ML workstream, you’ll work closely with business teams and data scientists to design, maintain, and improve machine learning applications. Your main responsibilities will include managing existing ML workloads and building new batch and on-demand pipelines to support advanced AI/ML models. You’ll also contribute to developing Generative AI solutions and applications for the emerging Agentic Era.


You’ll collaborate with a global team to create scalable data architectures optimised for AI/ML, source and prepare high-quality data, and implement robust ETL processes.


You should be comfortable working independently while driving improvements in engineering standards and best practices. As a senior member of the team, you will act as a mentor and advisor for junior engineers and take ownership as a project lead on strategic AI/ML initiatives.


Key Tasks

Design and maintain scalable data pipelines and storage systems for both agentic and traditional ML workloads.


Productionise LLM- and agent-based workflows, ensuring reliability, observability, and performance.
Build and maintain feature stores, vector/embedding stores, and core data assets for ML.
Develop and manage end-to-end traditional ML pipelines: data prep, training, validation, deployment, and monitoring.
Implement data quality checks, drift detection, and automated retraining processes.
Optimise cost, latency, and performance across all AI/ML infrastructure.
Collaborate with data scientists and engineers to deliver production-ready ML and AI systems.
Ensure AI/ML systems meet governance, security, and compliance requirements.
Mentor teams and drive innovation across both agentic and classical ML engineering practices.
Participate in team meetings and contribute to project planning and strategy discussions.

Education & Experience

Bachelor or Master’s degree in data science, data engineering, Computer Science with focus on math and statistics / Master’s degree is preferred.


At least 5 years experience as AI/ML data engineer undertaking above task and accountabilities.
Strong foundation in computer science principes and statistical methods.
Strong experience with cloud technology (AWS or Azure).
Strong experience with creation of data ingestion pipeline and ET process.
Strong knowledge of big data tool such as Spark, Databricks and Python.
Strong understanding of common machine learning techniques and frameworks (e.g. mlflow).
Strong knowledge of Natural language processing (NPL) concepts.
Strong knowledge of scrum practices and agile mindset.

Skills & Core competences

Strong Analytical and Problem-Solving Skills with attention to data quality and accuracy.


Clear Communication of technical concepts and effective collaboration across teams.
Adaptability to New Technologies and a proactive approach to learning and growth.
Team-Oriented Mindset, working closely with data scientists, AI engineers, and cross-functional teams.
Openness to Feedback and collective problem-solving for continuous improvement.
Team player, willing to improve yourself.

Our Offer

Flexi-Week and Work-Life Balance: We prioritise your mental health and well-being, offering you a flexible four-day Flexi-Week at full pay and with no reduction to your annual holiday allowance. We also offer a variety of different paid special leaves as well as volunteer days.


Remote Working Allowance: You will receive a monthly allowance to cover part of your running costs. In addition, we will support you in setting up your remote workspace appropriately.
Pension: Awin offers access to an additional pension insurance to all employees in Germany.
Flexi-Office: We offer an international culture and flexibility through our Flexi-Office and hybrid/remote work possibilities to work across Awin regions
Development: We’ve built our extensive training suite Awin Academy to cover a wide range of skills that nurture you professionally and personally, with trainings conveniently packaged together to support your overall development.
Appreciation: Thank and reward colleagues by sending them a voucher through our peer-to-peer program

Established in 2000, Awin is proud of our dynamic, social and inclusive culture.


Like all businesses, we’ve had to adapt and nurture our culture in a virtual environment. Our virtual ‘Life @ Awin’ hub brings our colleagues from across the globe together for various social activities.


Diversity & Inclusion are paramount to us, and we proudly pursue and hire diverse team members. We champion uniqueness and authenticity; this is who we are at our core. Our network of affiliate partnerships are diverse and transparent, as are the employees powering our vision to build the world’s leading open partner ecosystem. We welcome all backgrounds, identities, and experiences. If you need support at any point in the application or interview process, please let us know.


Awin is part of the Axel Springer group. Learn more at , and explore the Axel Springer Essentials here:


Apply now to begin the next stage of your career at a progressive company that supports both your professional and personal development.


#LI-RS1

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.