Machine Learning Tech Lead

Who you are

• This is a great role for someone who is still close to technical work. You might be a Tech Lead, Team Lead, Senior Machine Learning Scientist or Senior Machine Learning Engineer who has recently been building models and systems yourself, and is excited to grow into a broader leadership role
• You’ve recently worked as a hands-on Machine Learning practitioner, for example as a Senior IC, Tech Lead, Team Lead or ML manager
• You have strong foundations in ML, statistics, forecasting, optimisation or operations research, and enjoy applying them to messy real-world problems
• You’ve built, shipped or owned ML systems in production, and understand what it takes to make models useful, reliable, monitored and safe
• You can move between technical detail and business context, explaining complex ML ideas clearly to technical and non-technical people
• Coaching others is something you enjoy and take seriously. You may already have people management experience, but we’re also open to people taking their first formal step into management
• Ambiguous problem spaces energise you. The right answer might involve a model, an algorithm, a product change, an operational process, or a combination of all four
• Customer outcomes matter to you, and you want to build systems that help people get answers to their problems quickly and efficiently
• You’re adaptable, curious and excited by quantitative problems
• Not ticking every box? That’s totally okay! If you’re passionate about this role and keen to learn and grow with us, we encourage you to apply— even if you don’t have everything that's listed just yet. Drop us your application, we’d love to hear from you!

What the job involves

• We’re building the machine learning and optimisation systems that help Monzo provide fast, efficient and resilient customer support at scale
• You’ll lead Machine Learning for our Workforce Management vertical: the organisation that makes sure Monzo has the right human support supply to meet customer demand
• The team works on demand forecasting, real-time supply <> demand matching, problem routing and escalation, and predictive models that help us understand how long work will take, what might become late, and where the next best human support should be assigned
• Not everything here is about LLMs. You’ll work on rigorous ML, forecasting, optimisation and operations research problems where quantitative thinking, pragmatic delivery and strong product judgement really matter
• You’ll lead a growing team of Machine Learning Scientists, embedded in product squads working alongside data scientists, backend, mobile and web engineers, product managers, user researchers, designers and operations specialists
• 🔑You’ll play a key role by…
• Shaping the ML strategy for Workforce Management, helping us decide where ML, forecasting and optimisation can have the biggest impact for customers and human agents
• Leading the design, build and delivery of production ML systems across forecasting, routing, assignment, escalation and time-to-completion prediction
• Staying close to the technical details: helping the team frame problems well, choose the right modelling approach, define robust validation, review trade-offs and ship safely
• Working with Product, Engineering, Data and Operations leaders to turn ambiguous operational problems into pragmatic, iterative ML systems that improve real customer outcomes
• Coaching and developing Machine Learning Scientists through regular 1:1s, feedback and technical guidance
• Creating healthy ways of working for the team: clear priorities, strong technical standards, good documentation, sensible monitoring, and space for people to do their best work
• Contributing to the wider Machine Learning discipline at Monzo, helping us scale how we build, ship and operate ML systems in production

The application process

• 🌈 The application journey has 3 key steps:
• Recruiter Call
• Initial Call
• Full Loop: ML Modelling Skills Interview, Product & ML interview, Project Deep Dive Interview, Leadership interview
• This process should take around 3-4 weeks - your schedule is really important to us, so we promise to be as flexible as possible!
• If you have a preferred name, please use it to apply. We don't need full or birth names at application stage

Benefits

• Yearly Salary Review + Proactive pay increases to reflect growing responsibilities + Share options
• Flexible working hours
• Work From Home Opportunities + we'll pay for any equipment you need to do your job
• 24 days holiday a year, plus bank holidays
• Paid sick leave for both physical & mental health + Health insurance via AXA or Vitality + Additional health insurance through Medicash
• Employee Assistance Programme
• Learning budget of £1,000 a year
• Aviva Pension Scheme + 4% Employer Contribution
• Paid maternity, paternity, adoption or shared parental leave (after 13 weeks of employment)
• 52 weeks of primary caregiver leave (6 months at 100% pay, 13 weeks at Statutory Maternity Pay, 13 weeks unpaid)
• 3 months of secondary caregiver leave at 100% pay
• Unum Life Insurance
• Access to fertility and family-forming health benefits via Fertifa
• Abellio Season Travel Tickets + Cycle-to-Work Scheme