Role Description We’re looking for a Machine Learning Manager to lead ML specialists across different levels of seniority in the Fraud group. You’ll report to the Senior ML Manager for the FinCrime Collective, and you’ll help the team deliver high-impact ML systems while growing a healthy, inclusive and high-performing team. - Managing, coaching and developing a team of Machine Learning specialists, helping them do the best work of their careers through regular 1:1s, clear feedback and thoughtful development plans. - Helping the Fraud group focus ML effort on the highest-impact problems, balancing customer protection, regulatory expectations, operational impact and delivery pace. - Partnering with PMs, Risk Partners, Engineering Managers, Data Managers and senior ML/Data colleagues to shape roadmaps, unblock delivery and make clear trade-offs. - Creating the conditions for high-quality ML delivery: reliable model development, deployment, monitoring, governance, documentation and ownership across the full model lifecycle. - Holding a high bar for ML excellence in Fraud, including model performance, explainability, reproducibility, observability and safe operation in production. - Making sure ML products are owned, maintained and improved over time, rather than treated as one-off projects. - Building a strong team culture where people give and receive useful feedback, learn from each other, and work well with cross-functional partners. - Contributing to the wider Machine Learning and Data discipline at Monzo, including hiring, improving ways of working, and sharing patterns that help other teams move faster and more safely. Qualifications - You’ve managed Machine Learning specialists and have delivered through others and want to move further into people management. - You have strong ML judgement and can guide teams through the full model lifecycle, from problem framing and feature/model development through to deployment, monitoring, governance and iteration. - You’re comfortable working in ambiguous, high-impact problem spaces where there often isn’t one “right” answer. - You can build trusted relationships with Product, Risk, Engineering, Data and senior stakeholders, and you know how to bring the right people into the right conversations early. - You focus on outcomes over process, and can help a team prioritise work that protects customers and moves the business forward. - You know what good looks like for production ML systems, and you can raise the bar through coaching, review, standards and pragmatic decision-making. - You communicate clearly with technical and non-technical audiences, especially when explaining trade-offs, risks and recommendations. - You care deeply about building inclusive teams where people with different levels of experience can grow, challenge each other, and do high-quality work sustainably. - Experience in fraud, financial crime, risk, regulated environments or customer protection would be a bonus, but it’s not the only way to be successful here. Requirements - Not ticking every box? That’s totally okay! Studies show that women and people of colour might hesitate to apply unless they meet every single requirement. At Monzo, we’re dedicated to creating a diverse and welcoming team. - 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. Benefits - 💰 £113,200 - £153,200 + Incentive Awards tied to your performance. - ✈️ We can help you relocate to the UK. - ✅ We can sponsor your visa. - 📍This role can be based in our London office, but we're open to distributed working within the UK, with ad hoc meetings in London. - ⏰ We offer flexible working hours and trust you to work enough hours to do your job well, at times that suit you and your team. - 📚 £1,000 learning budget each year to use on books, training courses and conferences. - 🏡 We’ll set you up to work from home; all employees are given MacBooks and for fully remote workers we’ll provide extra support for your work-from-home setup. - ➕ Plus lots more! Read our full list of benefits.

Role Description We’re looking for a skilled ML Engineer / Data Scientist with 3+ years of software or applied ML experience to design, build, and improve machine learning solutions in a dynamic cloud environment, primarily on AWS. This role sits at the intersection of data science and engineering: - Exploring data, developing models, running rigorous experiments. - Bringing the best approaches into production with a reliable, reproducible workflow. If strong Python skills, curiosity about hard modeling problems, and collaborative work in multicultural teams are a fit, this is a chance to do meaningful, end-to-end ML work—not just notebooks, and not just infrastructure. Qualifications - Strong foundation in machine learning, statistics, and experiment design. - Experience building models for real business or product problems, not only academic benchmarks. - Comfortable working with structured and unstructured data: feature engineering, dataset construction, labeling quality, leakage checks, and train/validation/test discipline. - Able to compare approaches with clear metrics, error analysis, and sound judgment about tradeoffs (accuracy, latency, cost, maintainability). - Interest in modern ML, including classical ML, deep learning, and LLM / GenAI workflows where relevant (fine-tuning, RAG, evaluation, prompt/versioning). - Proficient in Python and able to write clean, modular, testable code. - Experience developing and deploying ML solutions in a cloud environment, especially AWS. - Comfortable moving from prototype to production: packaging models, building inference paths, monitoring performance, and iterating after launch. - Independent engineer who can own work from problem framing → experimentation → implementation → rollout. - Excellent written and spoken English. - Enjoy working closely with engineers, product partners, and other data scientists. - Clear communicator who can explain methods, results, and limitations to technical and non-technical audiences. - Master’s degree in Science or Engineering (Computer Science, Mathematics, Physics, Statistics, or similar), or equivalent practical experience. Requirements - Experience with scikit-learn, PyTorch, TensorFlow, XGBoost, or similar modeling stacks. - Familiarity with ML experiment tracking and reproducibility (e.g. MLflow, W&B). - Experience with SQL, data warehouses/lakes, and pipeline tools such as Airflow, dbt, or Spark. - Exposure to feature stores, embedding pipelines, or vector search for retrieval-based systems. - Experience building HTTP/gRPC APIs or lightweight services around model inference. - Working knowledge of Docker, basic orchestration, and CI/CD (e.g. GitLab CI). - Experience in agile, remote and async team environments. - Publications, patents, Kaggle/competition results, or open-source ML contributions. Benefits - Hands-on modeling work with room to explore, benchmark, and improve real systems. - Collaboration on ML patent submissions and participation in weekly ML / research paper review meetings. - A multicultural, engineering-focused team with strong peer support. - High trust and autonomy—clear goals, freedom in how to reach them. - Internal product impact: meaningful projects that improve developer and user experience, not endless maintenance tickets. - Short approval cycles and solid product partnership. - A healthy meeting policy and emphasis on protecting focus time. - Flexible hours, remote/home office options, and a calm, engineers-only office when on-site. - Competitive compensation, including stock options. - We’re hiring across multiple levels. Title, scope, and compensation depend on experience—from strong applied ML generalists to senior people who can lead modeling direction and mentor others. - We’re especially interested in candidates who are technically strong, intellectually curious, and motivated by difficult, ambiguous problems where good data science and solid engineering both matter.

Role Description The Inbound team develops highly-scalable solutions to determine item attribute values (e.g., color, material, style) and to verify merchandise authenticity. Senior Applied Scientists own the development and deployment of machine learning solutions and influence the technical direction of their team. They will work closely with tech-leads, Product and Engineering partners in the development of the solution. - Develop and deploy Computer Vision and Machine Learning solutions to solve business problems. - Maintain clean, efficient, and scalable code that meets industry standards. - Analyze large datasets to extract actionable insights and make informed decisions. - Employ state-of-the-art Machine Learning methodologies and frameworks to develop robust and scalable models. - Influence technical direction and take ownership of key components of systems and solutions, ensuring that they meet the needs of the business. - Collaborate with key stakeholders in the development of data-driven solutions and deployable products. - Mentor other team members to help establish team domain expertise. - Contribute to the company's intellectual property and technical leadership through patents and publications at top-tier conferences and journals. Qualifications - 5+ years of industry experience in Computer Vision and applied Machine Learning. - Masters Degree or PhD in CS / ML, statistics, or related field, or 8+ years of industry experience. - 3+ years experience in building, deploying, and managing machine learning and deep learning models in production environments at scale. - Deep understanding of Computer Vision and ML algorithms/techniques (CNNs, transformers, GANs, optimizers, regularization) and experiment design and best practices (A/B testing, training/serving pipelines, feature engineering). - Extensive experience in scientific libraries in Python (numpy, pandas) and Machine Learning tools and frameworks (PyTorch, Tensorflow, Keras, Scikit-Learn). - Strong data engineering skills and experience working with large scale datasets. - Experience with experiment automation frameworks (Ray Tune, W&B, Kubeflow). - Experience with cloud technologies AWS, GCP or Azure. - Fluency in Python. Requirements - PhD preferred (CS, ML, AI, Stats, OR or related field). - Background in applying ML techniques to solve real-world business problems in the retail sector. - Familiarity with MLOps tools and pipelines. - Impact-focused and passionate about delivering high-quality models.

• Development of machine learning models. • Data preparation and preprocessing. • Training, validation and optimization of models. • Deployment of models to production environments. • Performance monitoring and continuous improvement. • Collaboration with Data and business teams.