{"id":984811,"url":"https://alion.io/job/sumup-senior-data-scientistml-engineer-financial-crime","title":"Senior Data Scientist/ML Engineer - Financial Crime","company":{"id":2055,"name":"SumUp","domain":"sumup.com","url":"https://alion.io/company/sumup","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":{"grade":"B","score":79,"open_postings":100,"ghost_share":0,"stale_share":0.85,"repost_share":0,"time_to_fill_p50_days":45,"computed_at":"2026-10-19T05:45:15Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Berlin, Germany"],"countries":["DE"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":74000,"max_usd":153000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":15},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":true,"technologies":[{"name":"CI/CD","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"Anomaly Detection","optional":true},{"name":"Feature Store","optional":true},{"name":"pySpark","optional":true},{"name":"Spark","optional":true}],"status":"live","first_seen_at":"2026-08-20T07:09:41Z","employer_posted_date":"2026-09-22","last_verified_at":"2026-10-20T02:29:51Z","board_verified":true,"closed_at":null,"days_open":60,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":60},"description":"Help build ML systems that make financial crime harder to hide\nFinancial crime is constantly changing. New patterns and behaviours emerge all the time, and the data we work with is complex. Our work helps make financial activity safer and more trustworthy for merchants, customers, and SumUp.\nAs a Senior Data Science/ML Engineer in the Risk AI Engineering Squad, you will build the production systems that turn machine learning into reliable, explainable transaction-monitoring capabilities. You will work across the full model lifecycle: understanding financial-crime typologies, exploring data, engineering features, training and validating models, deploying them at scale, and monitoring their performance over time.\nThis role is designed for someone who is strongest on the engineering side of machine learning and wants to keep growing their data-science depth. You do not need to be a traditional data scientist or ML Engineer. We’re looking for someone who enjoys working across both disciplines: building robust, production-ready software while staying close to the data, models, and decisions those systems support.\nYou will join a cross-functional team within the Risk & Compliance tribe, working closely with AML and Fraud Operations, investigators, Product, and Engineering. Together, we build data products and ML solutions that help Risk teams work smarter, faster, and more effectively - while keeping our controls robust, auditable, and compliant across products and markets.\nWe actively welcome applications from women and people from underrepresented backgrounds. Diverse perspectives make our team stronger and our systems more robust. If you're motivated by technical depth, real-world impact, and the challenge of making ML work reliably in a high-stakes environment, this role is built for you.\nWhat you’ll do\nBuild ML systems that work in production\nOwn and evolve end-to-end batch training pipelines for transaction-monitoring models.\nBuild reliable software around the model lifecycle, including testing, CI/CD, versioning, deployment, monitoring, and rollback.\nImprove the maintainability, observability, and scalability of our model pipelines.\nPartner with platform and software engineers to make model delivery repeatable and safe.\nTurn data and domain knowledge into better detection\nBuild, maintain, and improve ML models for transaction monitoring, balancing detection quality, operational efficiency, explainability, and regulatory expectations.\nEngineer features that reflect AML and Fraud typologies and suspicious behaviours.\nWork with Risk investigators to translate domain knowledge into useful signals, alerting logic, and calibrated thresholds.\nAnalyse the drivers of the AML Risk Score and recommend improvements to its features, logic, and thresholds.\nKeep models trustworthy over time\nDefine and track meaningful model and operational metrics, including detection performance, alert volumes, and investigator outcomes.\nMonitor drift and model health, run back-testing, and investigate changes in performance.\nRun sensitivity tests on synthetic datasets and assess how models behave across relevant scenarios and populations.\nProduce model cards, technical documentation, and other ML governance artefacts that support auditability and regulatory review.\nContribute to system-design documentation and adapt solutions to regional compliance requirements.\nWork across disciplines\nPartner with AML and Fraud Operations, Product, and Engineering to turn ambiguous problems into clear, scalable technical plans.\nExplain trade-offs clearly to both technical and non-technical stakeholders.\nHelp the team improve its engineering practices, modelling approach, and understanding of financial-crime risk.\nShare what you learn and support a culture of thoughtful experimentation, constructive challenge, and continuous improvement.\nYou’ll be great for this role if you have…\nMust have\nStrong production Python engineering experience. You write code that ships and are comfortable with automated testing, CI/CD, code review, versioning, observability, and operating services or pipelines in production.\nExperience deploying and operating ML models in production. You understand the practical realities beyond experimentation: reproducible training, model versioning, deployment, monitoring, incident response, and rollback.\nHands-on experience with end-to-end ML pipelines. You have taken models from data preparation and training through validation and production use, and you understand how to choose appropriate KPIs and evaluation metrics.\nSolid data-engineering fundamentals. You have worked with complex, multi-source data ecosystems and care about data quality, lineage, reproducibility, and failure modes.\nA willingness to deepen your data-science expertise. You are interested in modelling, feature engineering, evaluation, and experimentation - even if your background is primarily in ML engineering or software engineering.\nClear, confident communication. You can align stakeholders, set expectations, surface risks, and turn ambiguous compliance or operational requirements into a concrete technical plan.\nNice to have\nExperience with PySpark or other distributed data-processing technologies.\nExperience in AML, fraud detection, transaction monitoring, or another financial-crime domain.\nExperience with unsupervised learning, such as anomaly detection or clustering.\nFamiliarity with feature stores, model registries, and alerting-threshold calibration.\nExperience producing ML governance artefacts, such as model cards, validation reports, or audit documentation.\nExperience with AI systems and tooling.\nThis role could be a strong fit if…\nYou are an ML engineer who wants to become more involved in modelling and data science.\nYou are a software or data engineer who has already shipped ML systems and wants to own more of the model lifecycle.\nYou are a data scientist who genuinely enjoys production engineering, automation, testing, and operating models - not only training them in notebooks.\nYou like working where technical decisions have a real-world impact and where reliability, explainability, and governance matter as much as model pe\nWhy you should join SumUp\n Opportunity to work with SumUppers globally on large-scale fintech products used by millions of businesses worldwide, from our Berlin office. This involves an office-first setup\n Commitment to Diversity and Inclusion: be part of a workplace that values and promotes diversity, fostering an inclusive environment where everyone's perspectives are respected and embraced\n Enrolment onto our Virtual Stock Option programme: you will own a stake in SumUp's future success\n A dedicated annual L&D budget of €2,000 for your individual development, which can be used to attend conferences and/or advance your career through further education\n A corporate pension scheme where we match up to 20% of your contributions\n Generous time off: enjoy 28 days of paid leave plus public holidays and special leave days\n Numerous other benefits such as Urban Sports Club subsidy, Kita placement assistance, and subsidised office lunches\n Break4me: 1-month sabbatical after 3 years of service\n Referral Bonus: earn additional rewards by referring talented individuals to join the SumUp team\nAbout SumUp\nBe empowered to do more that matters.\nAt SumUp, we're on a mission to empower small businesses across the globe by providing simple and affordable tools that allow them to thrive. Today, over 4 million businesses in 37 markets rely on SumUp as their financial partner to manage payments, finance and customer relationships.\nOur commitment to small businesses is reflected in our diverse teamof over 3,000 SumUppers from over 90 nationalities, united by global collaboration and an innovative mindset. Our core valueslay the foundation for who we are and what we stand for, shaping our work culture and driving our success. We foster inclusivity and a continuous learning culture, providing a safe space for personal and professional growth. Our differences make us unique and strong as we strive to create an environment where everyone belongs and feels supported, no matter how they identify.\nSumUp is proud to be an Equal Employment Opportunity employer, actively seeking and embracing diversity in our workforce. We don't make hiring or employment decisions based on race, colour, religion or religious belief, ethnic or national origin, nationality, sex, gender, gender identity, sexual orientation, disability, age or any other basis protected by applicable laws or prohibited by company policy. Our commitment extends beyond recruitment to creating a safe and respectful workplace where harassment of any form is strictly prohibited. Discover more about our culture and opportunities on our careers website, and follow our journey on LinkedIn, Instagram, and TikTok.\nJob Application Tip\nWe recognise that candidates feel they need to meet 100% of the job criteria in order to apply for a job. Please note that this is only a guide. If you don’t tick every box, it’s ok too because it means you have room to learn and develop your career at SumUp.","description_format":"text","description_chars":9154,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":["Continuous learning"],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["POS Systems","Payment Processing & Gateways"],"lifecycle":[{"event":"open","at":"2026-09-16T23:14:54Z"}],"visa":[],"liveness":{"score":22,"band":"cold","label":"Long shot","p_open":1,"p_active":0.493,"p_room":0.44,"age_days":59,"expected_fill_days":45,"reasons":["conf:2","stale_co","velocity","win:tail","crowd:brand"],"computed_at":"2026-10-19T05:45:15Z"},"pay":null,"html_url":"https://alion.io/job/sumup-senior-data-scientistml-engineer-financial-crime","json_url":"https://alion.io/job/sumup-senior-data-scientistml-engineer-financial-crime.json","meta":{"generated_at":"2026-10-20T02:53:00Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per call.","catalog":"https://alion.io/catalog.json","usage":{"tier":"crawler_verified","counted_by":"address","units_charged":1,"used_today":3173,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-10-21T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.02,"usd":0.002,"plus_per_slice":{"credits":0.05,"usd":0.005}},"unit":"per company, plus each slice with data","note":"the employer in depth","call":{"mcp_tool":"get_company","arguments":{"id":2055},"rest":"https://alion.io/mcp/rest/get_company?id=2055"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fsumup-senior-data-scientistml-engineer-financial-crime"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"live","price":{"credits":1,"usd":0.1},"unit":"per slice","note":"pay, demand and time to fill for this role and place","call":{"mcp_tool":"market_stats"},"human":"https://alion.io/catalog?offer=market.stats&for=job%2Fsumup-senior-data-scientistml-engineer-financial-crime"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.02,"usd":0.002},"unit":"per posting in a list","note":"similar open postings","call":{"mcp_tool":"search_jobs"},"human":"https://alion.io/catalog?offer=job.search&for=job%2Fsumup-senior-data-scientistml-engineer-financial-crime"},{"id":"company.verify","title":"Is this company real and active right now","status":"pilot","price":null,"unit":"per company","request":{"url":"https://alion.io/catalog/request","method":"POST","body":"{\"offer\": \"company.verify\", \"for\": \"job/sumup-senior-data-scientistml-engineer-financial-crime\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fsumup-senior-data-scientistml-engineer-financial-crime"}]}