Machine Learning Scientist (Fraud Detection)

Location: Spain; remote workplace, with an in-person meeting included in the hiring process.

Company

Mitek develops digital and biometric identity authentication, fraud prevention, and mobile deposit solutions using artificial intelligence, computer vision, and machine learning.

What you will do

• Design, develop, and evaluate machine learning models for fraud scoring, ensemble modeling, and decisioning.
• Analyze large-scale behavioral, relational, and contextual data to identify patterns, anomalies, and new fraud signals.
• Define experimentation and evaluation methods covering calibration, operating thresholds, and production performance metrics.
• Develop interpretable and explainable models, including human-in-the-loop and causal approaches.
• Collaborate with Product, Fraud Domain Experts, and Data Engineering to convert fraud challenges and prototypes into scalable production capabilities.
• Contribute to model monitoring, governance, documentation, ML tooling, and shared best practices.

Requirements

• Bachelor’s, Master’s, or PhD in Computer Science, Statistics, Mathematics, Machine Learning, or a related quantitative field, or equivalent practical experience.
• Approximately 5 years of experience as a Data Scientist, Applied ML Scientist, or in a comparable role.
• Strong knowledge of statistical methods, supervised machine learning, and model evaluation, particularly for highly imbalanced classification problems.
• Hands-on experience with feature engineering and model development using complex behavioral, relational, or contextual data.
• Strong programming and analytical skills with Python, PySpark, and SQL.
• Experience with cloud-based machine learning and data infrastructure.
• Strong spoken and written English; ability to collaborate with distributed teams across offices and time zones.
• Pragmatic, curious, collaborative, self-motivated, adaptable, and comfortable working autonomously, sharing knowledge, and mentoring colleagues.

Nice to have

• Experience in fraud detection, financial crime, payments, digital identity, risk, or Trust & Safety.
• Knowledge of graph-based methods, representation learning, or causal AI.
• Experience with Apache Iceberg, Apache Pinot, or other modern data lakehouse and real-time data technologies.
• Experience taking machine learning solutions from experimentation into scalable production environments.
• Confidence working in a Linux environment.

Culture & Benefits

• Remote-first flexibility with offices available and collaboration across distributed teams.
• Healthcare options, retirement or pension contributions, stock plan participation, life and disability coverage, and paid time off.
• Learning support through e-learning, tuition reimbursement, and hackathons.
• Home office setup allowance and optional benefits including pet insurance, identity theft protection, and legal assistance; offerings vary by geographic location.
• Career development in a collaborative environment focused on innovation, meaningful work, and measurable customer impact.

Hiring process

• The hiring process includes an in-person meeting.