Machine Learning Engineer

fifty-five
London
1 month ago
Create job alert

About Us:

fifty-five is a new kind of data company that helps brands leverage data to improve marketing, media, and customer experience through a combination of specialised consulting and technology services.

As the data and marketing pillar of the Brandtech Group, we offer services that blend strategy consulting, cloud services, media consulting, and customer experience expertise.

fifty-five comprises more than 400 digital experts. Digital consultants, tracking and media specialists, engineers, and data scientists work closely together to deliver top-tier marketing advice and technical assistance to brands across various industries, globally.

A partner to advertisers in data collection, activation, and utilisation, we help organisations become true omnichannel entities, mastering the efficiency of their digital ecosystem and its synergies with the physical world.

Based in London, we operate across three time zones from our 10 offices located in Paris, London, Geneva, Milan, Shanghai, Hong Kong, Shenzhen, Taipei, Singapore, and New York.

About the Role:

Within the Data Science team, you will actively participate in projects carried out by fifty-five on behalf of its clients. These projects encompass the application of machine learning methods to optimise site conversion rates and enhance the performance of the digital media mix for leading advertisers.

As an ML Engineer, you must master machine learning techniques to address a wide range of use cases while considering client activation constraints. Innovation will be central to your work, as you continuously enhance the performance of fifty-five’s offerings and adapt to new tools and constraints (such as privacy considerations).

You will collaborate closely with Data Engineers and Data Analysts and will play a role in helping them develop their skills.

Tasks & Responsibilities:

Design, develop, and maintain data science solutions for our clients (scoring models, time series models, attribution models, etc.). Conduct scientific research to foster innovation in daily projects. Implement data transformation and processing logic to ensure high-quality, reliable data is available for analysis and reporting. Monitor and troubleshoot data pipeline issues, ensuring timely resolution and minimal impact on business operations. Collaborate with internal consulting and client teams to understand data requirements and deliver data solutions that meet business needs. Effectively engage with clients at all levels, translating complex technical concepts into clear, actionable insights and maintaining strong relationships throughout project lifecycles.

Required Experience:

Graduated from a leading institution with a specialisation in a STEM field, you have 2–3 years of experience in data science. Demonstrated proficiency in data science missions and have successfully delivered complex machine learning projects

Required Skills:

Analytical and proactive mindset Proficient with both python and SQL Understanding of data science algorithms, how to train and evaluate them comprehensively Good working practices, such as code versioning and familiarity with CI/CD Ability to explain technical solutions clearly to non-technical or less technical stakeholders Some experience working in the cloud, such as GCP or AWS is a plus Fluency in English

If this sounds like you, please get in touch! We'd be delighted to speak with you.

In return, we can offer the following benefits:

Being part of a multicultural, dynamic and fast-growing team Continuous (and certified) training on the digital ecosystem and technologies (initial training for all new employees, followed by recurring training sessions) Private medical coverage through AXA Transport for London travel card allowance - covering 50% of zone 1-2 allowance The flexibility to work remotely for part of the week - this will continue post Covid 25 days holiday per year, in addition to UK bank and public holidays Company pension plan Company-sponsored sporting and social activities Monthly Codecademy subscription - reimbursable upon completion of chosen training path Cycle to Work Scheme

Related Jobs

View all jobs

Machine Learning Engineer

Machine Learning Engineer - Computer Vision

Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer

Get the latest insights and jobs direct. Sign up for our newsletter.

By subscribing you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Navigating Machine Learning Career Fairs Like a Pro: Preparing Your Pitch, Questions to Ask, and Follow-Up Strategies to Stand Out

Machine learning (ML) has swiftly become one of the most in-demand skill areas across industries, with companies leveraging predictive models and data-driven insights to solve challenges in healthcare, finance, retail, manufacturing, and beyond. Whether you’re an early-career data scientist aiming to break into ML, a seasoned engineer branching into deep learning, or a product manager exploring AI-driven solutions, machine learning career fairs offer a powerful route to connect with prospective employers face-to-face. Attending these events can help you: Network with hiring managers and technical leads who make direct recruitment decisions. Gain insider insights on the latest ML trends and tools. Learn about emerging job roles and new industry verticals adopting machine learning. Showcase your interpersonal and communication skills, both of which are increasingly important in collaborative AI/ML environments. However, with many applicants vying for attention in a bustling hall, standing out isn’t always easy. In this detailed guide, we’ll walk you through how to prepare meticulously, pitch yourself confidently, ask relevant questions, and follow up effectively to land the machine learning opportunity that aligns with your ambitions.

Common Pitfalls Machine Learning Job Seekers Face and How to Avoid Them

Machine learning has emerged as one of the most sought-after fields in technology, with companies across industries—from retail and healthcare to finance and manufacturing—embracing data-driven solutions at an unprecedented pace. In the UK, the demand for skilled ML professionals continues to soar, and opportunities in this domain are abundant. Yet, amid this growing market, competition for machine learning jobs can be fierce. Prospective employers set a high bar: they seek candidates with not just theoretical understanding, but also strong practical skills, business sense, and an aptitude for effective communication. Whether you’re a recent graduate, a data scientist transitioning into machine learning, or a seasoned developer pivoting your career, it’s essential to avoid common mistakes that may hinder your prospects. This blog post explores the pitfalls frequently encountered by machine learning job seekers, and offers actionable guidance on how to steer clear of them. If you’re looking for roles in this thriving sector, don’t forget to check out Machine Learning Jobs for the latest vacancies across the UK. In this article, we’ll break down these pitfalls to help you refine your approach in applications, interviews, and career development. By taking on board these insights, you can significantly enhance your employability, stand out from the competition, and secure a rewarding position in the world of machine learning.

Career Paths in Machine Learning: From Entry-Level Roles to Leadership and Beyond

Machine learning has rapidly transformed from an academic pursuit to a cornerstone of modern technology, fueling innovations in healthcare, finance, retail, cybersecurity, and virtually every industry imaginable. From predictive analytics and computer vision to deep learning models that power personalisation algorithms, machine learning (ML) is reshaping business strategies and creating new economic opportunities. As demand for ML expertise continues to outstrip supply, the UK has become a vibrant hub for machine learning research, entrepreneurship, and corporate adoption. Whether you’re just starting out or have experience in data science, software development, or adjacent fields, there has never been a better time to pursue a career in machine learning. In this article, we will explore: The growing importance of machine learning in the UK Entry-level roles that can kick-start your ML career The skills and qualifications you’ll need to succeed Mid-level and advanced positions, including leadership tracks Tips for job seekers on www.machinelearningjobs.co.uk By the end, you’ll have a clear view of how to build, grow, and lead in one of the most exciting fields in modern technology.