Be at the heart of actionFly remote-controlled drones into enemy territory to gather vital information.

Apply Now

Data Scientist (CX)

Depop
City of London
3 days ago
Create job alert

Join to apply for the Data Scientist (Marketplace Experience) role at Depop


Company Description


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. Find out more at 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. Just as our platform connects people globally, we believe our workplace should reflect the diversity of the communities we serve. We thrive on the power of different perspectives and experiences, knowing they drive innovation and bring us closer to our users. 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.


The Job

The Insights team works closely with stakeholders to produce analysis and to aid decision‑making. This role works directly with the customer experience team. The role of the data scientist supporting this team includes helping them understand the main levers with which we can influence customer experience, why customers are contacting us and how we can better improve our service.


There are several methods which the data scientist helps to achieve these goals. Firstly by making data available to consume for stakeholders. Secondly by collaborating with stakeholders on deep dives to analyse trends. Finally it is to champion and share synthesised learnings with stakeholders in adjacent teams so the wider business has an understanding of the opportunities and risks within the customer experience space.


Responsibilities

  • Creating and maintaining dashboards to track customer experience metrics.
  • Owning the migration of dashboarding to a scalable format.
  • Producing ETLs from raw data sources to make data surfaceable.
  • Aggregate data in a format which teams across the business can use.
  • Supporting customer experience teams with analytical requests.
  • Identifying areas of opportunity to explore to improve customer experience.
  • Measurement of impacts of strategic and tactical changes we make.
  • Root cause analysis when we see things going wrong.
  • Working across multiple teams including Community Support, Seller Success and Trust & Safety.
  • Managing multiple stakeholders and being comfortable with context switching.
  • Communicating and collaborating with Product Data Scientists and Product Managers in cross‑functional projects.

Requirements

  • Proficiency in SQL and the ability to work with large datasets.
  • Experience with visualisation tools like Looker or Tableau.
  • Adept at creating ETLs (added bonus for experience with DBT).
  • Ability to work across multiple contexts.
  • An analytical mind with good problem‑solving skills and a love of numbers.
  • Commercial awareness and a proactive attitude to make a difference and drive impact.
  • A hunger to learn, natural curiosity and a keen eye for detail.
  • A strong sense of ownership and highly organised nature.

Nice to haves

  • Experience working within a P2P marketplace or consumer‑facing mobile‑first products.
  • Knowledge of Zendesk.

Additional Information
Health & Mental Wellbeing

  • 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.

Work/Life Balance

  • 25 days annual leave with option to carry over up to 5 days.
  • 1 company‑wide day off per quarter.
  • Impact hours: Up to 2 days additional paid leave per year for volunteering.
  • Fully paid 4‑week sabbatical after completion of 5 years of consecutive service.
  • Flexible Working: MyMode hybrid‑working model with Flex, Office Based, and Remote options (role dependent).
  • All offices are dog‑friendly.
  • Ability to work abroad for 4 weeks per year in UK tax treaty countries.

Family Life

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

Learn & Grow

  • Budgets for conferences, learning subscriptions, and more.
  • Mentorship and programmes to upskill employees.

Your Future

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

Depop Extras

  • Employees enjoy free shipping on their Depop sales within the UK.
  • Special milestones are celebrated with gifts and rewards!

Seniority level

Entry level


Employment type

Full‑time


Job function

Engineering and Information Technology


London, England, United Kingdom


#J-18808-Ljbffr

Related Jobs

View all jobs

Data Scientist

Data Scientist

Data Scientist

Data Scientist

Data Scientist

Data Scientist

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 Hiring Trends 2026: What to Watch Out For (For Job Seekers & Recruiters)

As we move into 2026, the machine learning jobs market in the UK is going through another big shift. Foundation models and generative AI are everywhere, companies are under pressure to show real ROI from AI, and cloud costs are being scrutinised like never before. Some organisations are slowing hiring or merging teams. Others are doubling down on machine learning, MLOps and AI platform engineering to stay competitive. The end result? Fewer fluffy “AI” roles, more focused machine learning roles with clear ownership and expectations. Whether you are a machine learning job seeker planning your next move, or a recruiter trying to build ML teams, understanding the key machine learning hiring trends for 2026 will help you stay ahead.

Machine Learning Recruitment Trends 2025 (UK): What Job Seekers Need To Know About Today’s Hiring Process

Summary: UK machine learning hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise shipped ML/LLM features, robust evaluation, observability, safety/governance, cost control and measurable business impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for ML engineers, applied scientists, LLM application engineers, ML platform/MLOps engineers and AI product managers. Who this is for: ML engineers, applied ML/LLM engineers, LLM/retrieval engineers, ML platform/MLOps/SRE, data scientists transitioning to production ML, AI product managers & tech‑lead candidates targeting roles in the UK.

Why Machine Learning Careers in the UK Are Becoming More Multidisciplinary

Machine learning (ML) has moved from research labs into mainstream UK businesses. From healthcare diagnostics to fraud detection, autonomous vehicles to recommendation engines, ML underpins critical services and consumer experiences. But the skillset required of today’s machine learning professionals is no longer purely technical. Employers increasingly seek multidisciplinary expertise: not only coding, algorithms & statistics, but also knowledge of law, ethics, psychology, linguistics & design. This article explores why UK machine learning careers are becoming more multidisciplinary, how these fields intersect with ML roles, and what both job-seekers & employers need to understand to succeed in a rapidly changing landscape.