Senior Machine Learning Scientist

Role

Responsibilities

• You will:
• Research, design, and deliver robust machine learning solutions to tackle problems across the pricing and shipping space, spanning both prediction and decision-making under business constraints
• Lead efforts to productionise and scale these models, partnering with ML and backend engineers so they run reliably on our systems
• Identify and define requirements from multiple stakeholders across Pricing and Shipping, and lead the design of machine learning solutions to solve applied business problems
• Set up and conduct large-scale experiments to test hypotheses and guide model and product improvements, ensuring statistical rigour and real-world applicability
• Keep up to date with applied research, contribute to internal knowledge sharing and ML best practices, and apply new techniques for prediction, causal inference, and optimisation
• Participate in team ceremonies, such as technical whiteboarding sessions, planning, and roadmapping
• Communicate technical findings clearly and confidently to both technical and non-technical audiences

Qualifications

• Skills and Experience
• Significant experience working as a Machine Learning Scientist, with a proven track record of delivering and scaling models that solve complex, real-world problems
• Deep understanding of machine learning concepts and experience applying them in production settings, using frameworks such as PyTorch, TensorFlow, or gradient boosting libraries
• Experience with causal inference, uplift modelling, or optimisation under constraints, and comfort reasoning about decisions rather than predictions alone
• Strong Python skills, with the ability to write clean, modular, production-grade code, and a solid understanding of data engineering and MLOps principles
• Ability to lead end-to-end ML projects, work independently in ambiguous problem spaces, and mentor junior team members
• Strong collaboration and communication skills, with experience aligning technical approaches with cross-functional teams and stakeholders
• Bonus Points
• Experience with pricing models, promotions or incentive allocation, or revenue optimisation
• Experience with experiment design and conducting A/B tests
• Experience with bandits, reinforcement learning, or other explore-exploit approaches
• Experience with Databricks and PySpark
• Experience working with AWS or another cloud platform (GCP/Azure)
• 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 of annual leave with the option to carry over up to 5 days
• Impact hours: Up to 2 days of additional paid leave per year for volunteering
• Fully paid 4-week sabbatical after completion of 5 years of consecutive service with Depop, to give you a chance to recharge or do something you love.
• Flexible Working: MyMode hybrid-working model with Flex, Office-Based, and Remote options role-dependent
• All offices are dog-friendly
• Family Life:
• For birth parent: 20 weeks of paid parental leave for full-time regular employees
• For non-birth parents: 12 weeks of paid parental leave for full-time regular employees
• IVF leave, shared parental leave, and paid emergency parent/carer leave
• Learn + Grow:
• Twice-yearly development chats and yearly performance reviews
• Learning budget
• Upskilling our employees with company-wide training workshops, materials and resources
• Your Future:
• Life Insurance (financial compensation of 3x your salary)
• Pension matching up to 6% of full base salary with Aviva
• Depop Extras:
• In-office Depop Shop (that’s free!) and a packing station with free delivery.
• Special milestones are celebrated with gifts and rewards!

Preferred Qualifications

• Bonus Points