Applied Machine Learning Scientist London; United Kingdom

StackAdapt Inc.
London
2 months ago
Applications closed

Related Jobs

View all jobs

Principal Data Scientist & Machine Learning Researcher

Senior Data Scientist

Senior Machine Learning Engineer

Machine Learning Scientist

Machine Learning Scientist

Senior Data Scientist

Overview

StackAdapt is the leading technology company that empowers marketers to reach, engage, and convert audiences with precision. With 465 billion automated optimizations per second, the AI-powered StackAdapt Marketing Platform seamlessly connects brand and performance marketing to drive measurable results across the entire customer journey. The most forward-thinking marketers choose StackAdapt to orchestrate high-impact campaigns across programmatic advertising and marketing channels.

We are searching for a talented Data Scientist to join our engineering team as we continue to expand our data science efforts. Our platform is connected to thousands of publishers and advertisers worldwide and as a result, we\'re dealing with millions of requests each second, making billions of decisions. We utilize the latest technologies to solve challenges in traffic, data storage, machine learning, and scalability.

StackAdapt is a Remote First company, and we are open to candidates located anywhere in the United Kingdom for this position.

What you\'ll be doing:
  • Innovate ML algorithms to maximize ROI and advertising performance. This ranges from creating entirely new algorithms, to improvements on state-of-the art methods, to development using a deep understanding of classic methods
  • Write production code, sometimes collaborating with Data Engineers, to implement the novel ML algorithms
  • Prototype potential algorithms and pipelines, test them using historical data, and iterate to modify based on insights
What you\'ll bring to the table:
  • Have a Masters degree or PhD in Computer Science, Statistics, Operations Research, or a related field, with dual degrees a plus.
  • Have the ability to take an ambiguously defined task, and break it down into actionable steps
  • Have a comprehensive understanding of statistics, optimization and machine learning
  • Are proficient in coding, data structures, and algorithms
  • Enjoy working in a friendly, collaborative environment with others
StackAdapter\'s Enjoy:
  • Highly competitive salary
  • RRSP/401K matching
  • 3 weeks vacation + 3 personal care days + 1 Culture & Belief day + birthdays off
  • Access to a comprehensive mental health care platform
  • Health benefits from day one of employment
  • Work from home reimbursements
  • Optional global WeWork membership for those who want a change from their home office
  • Robust training and onboarding program
  • Coverage and support of personal development initiatives (conferences, courses, etc)
  • Access to StackAdapt programmatic courses and certifications to support continuous learning
  • An awesome parental leave policy
  • A friendly, welcoming, and supportive culture
  • Our social and team events!

StackAdapt is a diverse and inclusive team of collaborative, hardworking individuals trying to make a dent in the universe. No matter who you are, where you are from, who you love, follow in faith, disability (or superpower) status, ethnicity, or the gender you identify with (if you\’re comfortable, let us know your pronouns), you are welcome at StackAdapt. If you have any requests or requirements to support you throughout any part of the interview process, please let our Talent team know.

About StackAdapt

We\'ve been recognized for our diverse and supportive workplace, high performing campaigns, award-winning customer service, and innovation. We\'ve been awarded:

Interested in building your career at StackAdapt? Get future opportunities sent straight to your email.

Apply for this job

indicates a required field

First Name *

Last Name *

Preferred First Name

Email *

Phone *

Location (City) *

Resume/CV *

Enter manually

Accepted file types: pdf, doc, docx, txt, rtf

LinkedIn Profile

If hired by StackAdapt, do you intend to hold any secondary employment, advisory position (e.g. membership on a board of directors), or volunteer position that (1) is on behalf of a business that would be competitive in nature to the business of StackAdapt, (2) conflict with your ability to perform your duties at StackAdapt, or (3) conflict with your working hours at StackAdapt * Select...

What are your salary expectations (annually)? *

Do you now or will you in the future require StackAdapt to sponsor you for authorization to work in the country the job you are applying for is primarily located? * Select...

Have you previously worked at StackAdapt? * Select...

What was your Master\'s/PhD Major? *

What was the University where you completed your Master\'s/PhD? *

What was your undergraduate major? *

What was the university where you completed your undergraduate degree? *

What was your GPA during your undergraduate degree? *

What was your GPA during your Masters/PhD degree? *

GitHub

If you have a GitHub, please share your link


#J-18808-Ljbffr

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.

Machine Learning Jobs for Career Switchers in Their 30s, 40s & 50s (UK Reality Check)

Are you considering a career change into machine learning in your 30s, 40s or 50s? You’re not alone. In the UK, organisations across industries such as finance, healthcare, retail, government & technology are investing in machine learning to improve decisions, automate processes & unlock new insights. But with all the hype, it can be hard to tell which roles are real job opportunities and which are just buzzwords. This article gives you a practical, UK-focused reality check: which machine learning roles truly exist, what skills employers really hire for, how long retraining realistically takes, how to position your experience and whether age matters in your favour or not. Whether you come from analytics, engineering, operations, research, compliance or business strategy, there is a credible route into machine learning if you approach it strategically.

How to Write a Machine Learning Job Ad That Attracts the Right People

Machine learning now sits at the heart of many UK organisations, powering everything from recommendation engines and fraud detection to forecasting, automation and decision support. As adoption grows, so does demand for skilled machine learning professionals. Yet many employers struggle to attract the right candidates. Machine learning job adverts often generate high volumes of applications, but few applicants have the blend of modelling skill, engineering awareness and real-world experience the role actually requires. Meanwhile, strong machine learning engineers and scientists quietly avoid adverts that feel vague, inflated or confused. In most cases, the issue is not the talent market — it is the job advert itself. Machine learning professionals are analytical, technically rigorous and highly selective. A poorly written job ad signals unclear expectations and low ML maturity. A well-written one signals credibility, focus and a serious approach to applied machine learning. This guide explains how to write a machine learning job ad that attracts the right people, improves applicant quality and strengthens your employer brand.

Maths for Machine Learning Jobs: The Only Topics You Actually Need (& How to Learn Them)

Machine learning job adverts in the UK love vague phrases like “strong maths” or “solid fundamentals”. That can make the whole field feel gatekept especially if you are a career changer or a student who has not touched maths since A level. Here is the practical truth. For most roles on MachineLearningJobs.co.uk such as Machine Learning Engineer, Applied Scientist, Data Scientist, NLP Engineer, Computer Vision Engineer or MLOps Engineer with modelling responsibilities the maths you actually use is concentrated in four areas: Linear algebra essentials (vectors, matrices, projections, PCA intuition) Probability & statistics (uncertainty, metrics, sampling, base rates) Calculus essentials (derivatives, chain rule, gradients, backprop intuition) Basic optimisation (loss functions, gradient descent, regularisation, tuning) If you can do those four things well you can build models, debug training, evaluate properly, explain trade-offs & sound credible in interviews. This guide gives you a clear scope plus a six-week learning plan, portfolio projects & resources so you can learn with momentum rather than drowning in theory.