Associate Data Analyst, Revenue

Bumble Inc.
London
1 month ago
Applications closed

Related Jobs

View all jobs

BI and Data Analyst

Data Science and Analytics Senior Business Analyst

Engineering Parts Data Analyst

Engineering Parts Data Analyst

Master Data Manager

Data Analyst

Inclusion at Bumble Inc.

Bumble Inc. is an equal opportunity employer and we strongly encourage people of all ages, colour, lesbian, gay, bisexual, transgender, queer and non-binary people, veterans, parents, people with disabilities, and neurodivergent people to apply. We're happy to make any reasonable adjustments that will help you feel more confident throughout the process, please don't hesitate to let us know how we can help.

In your application, please feel free to note which pronouns you use (For example: she/her, he/him, they/them, etc).

We’re looking for curious analysts interested in being considered for a Data Analyst role in the Advanced Data Analytics team and who are buzzing to solve impactful questions for exciting products in the online dating industry and beyond.

In this role, you’ll support our stakeholders with data and insights related to a variety of business areas. You’ll be involved in experimentation design and planning, visualisations, trend diagnostics, dashboarding and much, much more.

What you'll do:

  • You’ll have access to the most diverse, varied and complex data sets in the technology world right now (10’s of billions of data points per day!!)
  • You’ll be doing all the typical day-to-day tasks such as generating practical insights from the data, manipulating complex datasets, helping us understand how to improve and optimise our members experience to facilitate the creation of meaningful, equitable and healthy relationships.
  • Unlock your potential by upskilling in SQL, Python and other essential tools, empowering you to expand your skill set and excel in your career.
  • You will help uncover tactical and strategic insights and support experimentation in a range of areas such as revenue management, operations and strategy. For instance, you could work on testing and validating pricing and promotion based opportunities across our portfolio of apps.
  • You will make contributions to the data model and could contribute to building out data pipelines.
  • You’ll collaborate closely with several potential teams including Product Managers, strategists, operations managers, and Data Engineers, building detailed, entrepreneurial and business-minded insights that directly contribute to the successful growth of Bumble Inc.

You have confidence in the following:

  • You are a data analyst with expertise in SQL, data visualisation, A/B testing, statistical knowledge and storytelling, etc, is entrepreneurial, gets excited about building from scratch, and is relentlessly experimenting in pursuit of building a product that members love.
  • Supporting experimentation analytics (e.g. A/B testing, user group testing): significance analysis, non-parametric methods, bootstrapping, causal inference.
  • Manipulating complex datasets using a range of tools such as SQL and Hive.
  • Building effective self-serve tools and visualisations to help optimise decision making.
  • Engaging and collaborating with key stakeholders such as developers, product managers, and senior management.
  • Identifying issues and opportunities, translating business problems into analytical diagnoses and delivering insights that answer the most pressing questions.
  • Supporting engineers and teammates in improving data quality, and specifying requirements for new data collection.
  • Partnering with the wider analytics/data science team to solve business problems.
  • Experience in Python is desirable but not required.
  • Experience with data modelling and making domain-specific data models is a plus.

About Us

Bumble Inc. is the parent company of Bumble, Badoo, Bumble For Friends, and Geneva. The Bumble platform enables people to build healthy and equitable relationships, through Kind Connections. Founded by Whitney Wolfe Herd in 2014, Bumble was one of the first dating apps built with women at the center and connects people across dating (Bumble Date), friendship (Bumble For Friends) and professional networking (Bumble Bizz). Badoo, which was founded in 2006, is one of the pioneers of web and mobile dating products. Bumble For Friends is a friendship app where people in all stages of life can meet people nearby and create meaningful platonic connections. Geneva is a group and community app for people to connect based on shared interests.

#J-18808-Ljbffr

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.

Machine Learning Jobs in the Public Sector: Opportunities Across GDS, NHS, MOD, and More

Machine learning (ML) has rapidly moved from academic research labs to the heart of industrial and governmental operations. Its ability to uncover patterns, predict outcomes, and automate complex tasks has revolutionised industries ranging from finance to retail. Now, the public sector—encompassing government departments, healthcare systems, and defence agencies—has become an increasingly fertile ground for machine learning jobs. Why? Because government bodies oversee vast datasets, manage critical services for millions of citizens, and must operate efficiently under tight resource constraints. From using ML algorithms to improve patient outcomes in the NHS, to enhancing cybersecurity within the Ministry of Defence (MOD), there’s a growing demand for skilled ML professionals in UK public sector roles. If you’re passionate about harnessing data-driven insights to solve large-scale problems and contribute to societal well-being, machine learning jobs in the public sector offer an unparalleled blend of challenge and impact. In this article, we’ll explore the key reasons behind the public sector’s investment in ML, highlight the leading organisations, outline common job roles, and provide practical guidance on securing a machine learning position that helps shape the future of government services.

Contract vs Permanent Machine Learning Jobs: Which Pays Better in 2025?

Machine learning (ML) has swiftly become one of the most transformative forces in the UK technology landscape. From conversational AI and autonomous vehicles to fraud detection and personalised recommendations, ML algorithms are reshaping how organisations operate and how consumers experience products and services. In response, job opportunities in machine learning—including roles in data science, MLOps, natural language processing (NLP), computer vision, and more—have risen dramatically. Yet, as the demand for ML expertise booms, professionals face a pivotal choice about how they want to work. Some choose day‑rate contracting, leveraging short-term projects for potentially higher immediate pay. Others embrace fixed-term contract (FTC) roles for mid-range stability, or permanent positions for comprehensive benefits and a well-defined career path. In this article, we will explore these different employment models, highlighting the pros and cons of each, offering sample take‑home pay scenarios, and providing insights into which path might pay better in 2025. Whether you’re a new graduate with a machine learning degree or an experienced practitioner pivoting into an ML-heavy role, understanding these options is key to making informed career decisions.

Machine‑Learning Jobs for Non‑Technical Professionals: Where Do You Fit In?

The Model Needs More Than Math When ChatGPT went viral and London start‑ups raised seed rounds around “foundation models,” many professionals asked, “Do I need to learn PyTorch to work in machine learning?” The answer is no. According to the Turing Institute’s UK ML Industry Survey 2024, 39 % of advertised ML roles focus on strategy, compliance, product or operations rather than writing code. As models move from proof‑of‑concept to production, demand surges for specialists who translate algorithms into business value, manage risk and drive adoption. This guide reveals the fastest‑growing non‑coding ML roles, the transferable skills you may already have, real transition stories and a 90‑day action plan—no gradient descent necessary.