Data Science Manager, Product & CX (12 month FTC)

Depop
City of London
4 months ago
Applications closed

Related Jobs

View all jobs

frog -Managing Consultant - Data Science (Customer Data)

frog - Senior Consultant - Data Science (Customer Data)

Data Science Manager – Property Tech – London

Data Science Manager - Property Tech - London

Data Science Manager – Property Tech – London

Data Science Manager (GenAI)

Data Science Manager, Product & CX (12 month FTC)

Depop is the community‑powered circular fashion marketplace where anyone can buy, sell and discover desirable secondhand fashion. With a community of over 35 million users, Depop is on a mission to make fashion circular, redefining fashion consumption. Founded in 2011, the company is headquartered in London, with offices in New York and Manchester, and in 2021 became a wholly‑owned subsidiary of Etsy. www.depop.com


Our mission is to make fashion circular and to create an inclusive environment where everyone is welcome, no matter who they are or where they’re from. We’re proud to be an equal opportunity employer, providing employment opportunities without regard to age, ethnicity, religion or belief, gender identity, sex, sexual orientation, disability, pregnancy or maternity, marriage and civil partnership, or any other protected status. We’re continuously evolving our recruitment processes to ensure fairness and are open to accommodating any needs you might have.


If, due to a disability, you need adjustments to complete the application, please let us know by sending an email with your name, the role to which you would like to apply, and the type of support you need to complete the application to . For any other non‑disability related questions, please reach out to our Talent Partners.


Key Responsibilities

  • Partner with the Senior Director of Product (Marketplace) to identify opportunities for shipping, payments, trust and safety and customer experience.
  • Provide analytical leadership within marketplace insights: Provide direction and inspiration for exploratory work—executed either by your team or as your own individual contributor projects (our platform stack is Databricks, DBT, Optimizely, Looker).
  • Step in to fill capacity gaps, especially when product data science teams experience spikes in workload, to unblock critical initiatives.
  • Lead, mentor, and develop a team of four direct reports, fostering a culture of collaboration, innovation, and analytical excellence.
  • Contribute to the broader product data science team by sharing best practices and championing high‑quality analytics.

Requirements

  • Exemplary problem solving skills, with particular strength in creating meaningful analysis from ambiguous questions.
  • Strong competency in verbal and written communication of results to a wide variety of stakeholder levels.
  • Experience managing teams of 2 to 4 people whilst also contributing as an individual contributor.
  • A high degree of independence and ability to manage upwards to senior stakeholders.
  • Thrives when working across multiple projects.
  • Expertise in SQL and the ability to work with large datasets.
  • Experience in Python and command over ETL scripts.

Benefits

  • PMI and cash plan healthcare access with Bupa.
  • Subsidised counselling and coaching with Self Space.
  • Cycle to Work scheme with options from Evans or the Green Commute Initiative.
  • Employee Assistance Programme (EAP) for 24/7 confidential support.
  • Mental Health First Aiders across the business for support and signposting.


  • 25 days annual leave with option to carry over up to 5 days.
  • 1 company‑wide day off per quarter.
  • Flexible Working: MyMode hybrid‑working model with Flex, Office Based, and Remote options *role dependant.
  • All offices are dog‑friendly.
  • Ability to work abroad for 4 weeks per year in UK tax treaty countries.


  • 18 weeks of paid parental leave for full‑time regular employees.
  • IVF leave, shared parental leave, and paid emergency parent/carer leave.


  • Twice yearly development chats and yearly performance reviews.
  • Learning budget.
  • Upskilling our employees with company wide training workshops, materials and resources.


  • Life Insurance (financial compensation of 3x your salary).
  • Pension matching up to 6% of qualifying earnings.


#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 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.