Senior Data Scientist, Recommendations

Bumble Inc.
London
1 year ago
Applications closed

Related Jobs

View all jobs

Senior Data Scientist

Senior Data Scientist - National Security (TIRE) based in Cheltenham/H

Senior Data Scientist

Senior Data Scientist

Junior / Graduate Data Scientist

Data Scientist

Bumble is looking for a Senior Data Scientist to join our team and play a key role in fulfilling our mission to create a world where all relationships are healthy and equitable. Concretely, this means exploring our large datasets, developing statistical models and designing data-driven strategies for products that provide a safe and engaging experience for our users, and improve the way Bumble operates.

Interested in learning more about this job Scroll down and find out what skills, experience and educational qualifications are needed.

With millions of images and messages exchanged on our platform every day, there is a wealth of opportunity to make a real difference in this role and help people find love all over the world! The ideal candidate combines strong business acumen, extensive experience in data science and advanced analytics along with a passion for tech.

THE RECOMMENDATIONS TEAM

We are part of the cross-functional Recommendations group at Bumble Inc., a team of passionate engineers, scientists, and machine learning professionals who focus on designing and building products that power our mission of "creating a world where all relationships are healthy and equitable, through Kind Connections." We partner with wider business stakeholders, Product, and other Engineering teams to build state-of-the-art recommendation systems for our portfolio of apps, including Bumble, Badoo, BFF, and Fruitz. We are passionate about improving the experience of our members through leveraging AI and Machine Learning in our products.

WHAT YOU WILL BE DOING

Work in a cross-functional team alongside machine learning scientists and machine learning engineersWork out where the most value is and help set up frameworks for evaluating algorithmic improvementsSet up and conduct large-scale experiments to test hypotheses and drive product developmentAssess impact of algorithm changes on marketplace dynamicsPartner with business functions and engineering teams to help frame problems into scalable AI solutions and solve key problems by leveraging the large and complex datasets at our disposalCollaborate with Product Management to establish roadmaps and define key metrics to optimise for alignment with Bumble's strategic objectivesDrive a culture of insightful storytelling across the businessKeep up with state-of-the-art research with the opportunity to create prototypes for the business and present at top conferences>WE'D LOVE TO MEET SOMEONE WITHA degree in Computer Science, Mathematics or a similar quantitative discipline like economics or social scienceStrong statistical modelling background - hypotheses testing, inference, regressions, random variablesComfortable presenting back to technical and non-technical stakeholders through effective data visualisation and building of reporting frameworksComfortable with Python data science libraries such as pandas, scikit-learn, numpy, statsmodelsStrong SQL experience including analytic functions, performance tuning, data wranglingAbility to work collaboratively and proactively in a fast-paced environment alongside scientists, engineers and non-technical stakeholdersAbility to combine business intuition with the application of advanced solutionsA passion for keeping up with the latest ongoings in Data Science and Machine Learning communitiesA curious mind, self-starter and endlessly keen to learn and develop themselves professionally

AN ADDED BONUS IF YOU HAVEAn understanding of multi-sided markets and/or dating problem spaceExperience in using advanced statistical methods to solve problems. This can either be through academic projects and publications, or experience analysing and solving problems within industryUnderstanding of Machine Learning development lifecycleHands-on experience in delivering Machine Learning modelsA basic knowledge of software development life cycle processes and tools - ETL pipelines, CI/CD, MLOps, agile methodologies, version control (git), testing frameworks>

#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.

How Many Machine Learning Tools Do You Need to Know to Get a Machine Learning Job?

Machine learning is one of the most exciting and rapidly growing areas of tech. But for job seekers it can also feel like a maze of tools, frameworks and platforms. One job advert wants TensorFlow and Keras. Another mentions PyTorch, scikit-learn and Spark. A third lists Mlflow, Docker, Kubernetes and more. With so many names out there, it’s easy to fall into the trap of thinking you must learn everything just to be competitive. Here’s the honest truth most machine learning hiring managers won’t say out loud: 👉 They don’t hire you because you know every tool. They hire you because you can solve real problems with the tools you know. Tools are important — no doubt — but context, judgement and outcomes matter far more. So how many machine learning tools do you actually need to know to get a job? For most job seekers, the real number is far smaller than you think — and more logically grouped. This guide breaks down exactly what employers expect, which tools are core, which are role-specific, and how to structure your learning for real career results.

What Hiring Managers Look for First in Machine Learning Job Applications (UK Guide)

Whether you’re applying for machine learning engineer, applied scientist, research scientist, ML Ops or data scientist roles, hiring managers scan applications quickly — often making decisions before they’ve read beyond the top third of your CV. In the competitive UK market, it’s not enough to list skills. You must send clear signals of relevance, delivery, impact, reasoning and readiness for production — and do it within the first few lines of your CV or portfolio. This guide walks you through exactly what hiring managers look for first in machine learning applications, how they evaluate CVs and portfolios, and what you can do to improve your chances of getting shortlisted at every stage — from your CV and LinkedIn profile to your cover letter and project portfolio.

MLOps Jobs in the UK: The Complete Career Guide for Machine Learning Professionals

Machine learning has moved from experimentation to production at scale. As a result, MLOps jobs have become some of the most in-demand and best-paid roles in the UK tech market. For job seekers with experience in machine learning, data science, software engineering or cloud infrastructure, MLOps represents a powerful career pivot or progression. This guide is designed to help you understand what MLOps roles involve, which skills employers are hiring for, how to transition into MLOps, salary expectations in the UK, and how to land your next role using specialist platforms like MachineLearningJobs.co.uk.