{"id":3405844,"url":"https://alion.io/job/mitek-systems-machine-learning-scientist","title":"Machine Learning Scientist","company":{"id":680208,"name":"Mitek Systems","domain":"miteksystems.com","url":"https://alion.io/company/miteksystems","size_band":"201-500","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Lever","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":"full_time","work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Spain"],"countries":["ES"],"hiring_countries":["ES"],"hiring_countries_total":1,"salary":{"min":50000,"max":70000,"currency":"EUR","period":"year","gross":null,"usd_annual":77998},"salary_estimate":null,"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Human-in-the-Loop","optional":false},{"name":"Machine Learning","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"SQL","optional":false},{"name":"Apache Iceberg","optional":true},{"name":"Linux","optional":true},{"name":"Spark","optional":true}],"status":"live","first_seen_at":"2026-10-28T12:42:36Z","employer_posted_date":"2026-10-28","last_verified_at":"2026-10-30T02:37:39Z","board_verified":true,"closed_at":null,"days_open":1,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":1},"description":"As a member of Mitek’s team, you will build machine learning solutions that improve fraud detection and explainability across our digital identity products.\nYou will combine different sourcesof evidence, explore complex patterns and relationships in large-scale data, and develop reliable models that support better fraud decisions.\nYou will work closely with Product and Fraud Domain Experts to understand emerging fraud challenges and collaborate with Data Engineering to turn promising ideas into scalable production capabilities.\nWe are looking for a hands-on Senior Data Scientist with strong machine learning fundamentals, curiosity to explore new approaches, and a practical mindset focused on delivering measurable business value.\nWhat You Will Do\nDesign, develop, and evaluate machine learning models for fraud detection, including fraud scoring and ensemble models that combine multiple sources of evidence.\n\nAnalyze large-scale datasets to identify complex patterns, relationships, behaviors, and anomalies, developing new signals to improve fraud detection.\n\nDefine robust experimentation and evaluation methodologies, including model calibration, operating thresholds, and performance metrics under realistic production conditions.\n\nDevelop interpretable and explainable ML approaches that help internal stakeholders and customers understand fraud scores and decisions.\n\nExplore innovative approaches to fraud decisioning, including human-in-the-loop techniques and causal methods.\n\nCollaborate with Product and Fraud Domain Experts to translate real-world fraud challenges and production feedback into effective machine learning capabilities.\n\nPartner with Data Engineering to transform ML prototypes into scalable, maintainable, production-ready solutions.\n\nContribute to model monitoring, governance, technical documentation, ML tooling, and shared best practices across the team.\n\nWhat You Need\nA Bachelor's, Master's, or PhD degree in Computer Science, Statistics, Mathematics, Machine Learning, or a related quantitative field, or equivalent practical experience.\n\nSkills and expertise typically gained through approximately 5 years of experience as a Data Scientist, Applied ML Scientist, or in a comparable role.\n\nStrong knowledge of statistical methods, supervised machine learning, and model evaluation, particularly for highly imbalanced classification problems.\n\nHands-on experience in feature engineering and machine learning model development using complex behavioral, relational, or contextual data.\n\nStrong programming and analytical skills using Python, PySpark, and SQL.\n\nExperience working with cloud-based machine learning and data infrastructure.\n\nWhat Would Be Nice\nExperience in fraud detection, financial crime, payments, digital identity, risk, or Trust & Safety.\nKnowledge of advanced machine learning approaches such as graph-based methods, representation learning, or causal AI.\nExperience with modern data lakehouse or real-time data technologies such as Apache Iceberg or Apache Pinot.\nExperience taking machine learning solutions from experimentation into scalable production environments.\nConfidence working in Linux environment.\nWho You Are\nA pragmatic and hands-on machine learning practitioner who focuses on solving meaningful business problems rather than adding unnecessary model complexity.\n\nNaturally curious, with a passion for discovering patterns and relationships in data and experimenting with new approaches.\n\nComfortable using data and evidence to challenge assumptions and influence technical and product decisions.\n\nCollaborative and willing to share knowledge, mentor colleagues, and contribute to continuous improvement.\n\nA confident communicator with strong spoken and written English skills.\n\nComfortable collaborating with distributed teams across different offices and time zones.\n\nSelf-motivated, adaptable, and able to work autonomously while contributing positively to a team.\n\nWhat Success Looks Like\nDeliver measurable impact: Develop and improve machine learning models that strengthen fraud detection, support more accurate decisions, and deliver tangible business value.\n\nTurn insights into innovation: Identify meaningful patterns, behaviors, and relationships in complex datasets and translate them into effective fraud detection signals.\n\nBuild reliable, explainable ML solutions: Create models that are robust, interpretable, and evaluated against realistic production conditions.\n\nTake ideas into production: Work closely with Data Engineering to transform promising ML experiments into scalable, maintainable solutions.\n\nDrive continuous improvement: Use experimentation, production feedback, and emerging ML techniques to improve model performance and fraud decisioning.\n\nMake an impact through collaboration: Partner with Product, Fraud Domain Experts, and technical teams to solve complex challenges, share knowledge, and advance ML best practices.","description_format":"text","description_chars":4933,"description_truncated":false,"requirements":{"experience_years_min":5,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":false},"security_clearance":false,"languages":[{"language":"English","level":"All 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