Machine Learning Scientist

Mitek (NASDAQ: MITK) is a global leader in digital & biometric identity authentication, fraud prevention, and mobile deposit solutions. Our verified identity platform and advanced image capture solutions are built on the latest advancements in biometric recognition, artificial intelligence, computer vision and machine learning, and trusted by over 7,500 organizations worldwide. We are headquartered in San Diego, California, with operations in the United Kingdom, Spain, France, Mexico, and the Netherlands. Visit us at www.miteksystems.com.

We are Virtual 1st! Whether you choose to work remotely from your home office or in-person from one of Mitek's offices, our practices, processes and tools are designed to enable your success. At Mitek, the Future of Work is about flexibility and preference wherever and whenever we are working. Because we care about our candidates, employees and customers, we include an in-person meeting as part of our hiring process. It's one of the ways we live our mission to "Protect What's Real."

At Mitek, we believe that teams are more resilient, effective, and innovative when they benefit from a wide range of ideas, lived experiences, and perspectives. The strength of our organization is deeply rooted in the people who power it. We know that a workforce reflecting the richness of our communities and customers helps us better serve their needs.

As a member of Mitek's team, you will build machine learning solutions that improve fraud detection and explainability across our digital identity products.

You will combine different sources of evidence, explore complex patterns and relationships in large-scale data, and develop reliable models that support better fraud decisions.

You 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.

We 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.

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

• A pragmatic and hands-on machine learning practitioner who focuses on solving meaningful business problems rather than adding unnecessary model complexity.

• Naturally curious, with a passion for discovering patterns and relationships in data and experimenting with new approaches.

• Comfortable using data and evidence to challenge assumptions and influence technical and product decisions.

• Collaborative and willing to share knowledge, mentor colleagues, and contribute to continuous improvement.

• A confident communicator with strong spoken and written English skills.

• Comfortable collaborating with distributed teams across different offices and time zones.

• Self-motivated, adaptable, and able to work autonomously while contributing positively to a team.

• Deliver measurable impact: Develop and improve machine learning models that strengthen fraud detection, support more accurate decisions, and deliver tangible business value.

• Turn insights into innovation: Identify meaningful patterns, behaviors, and relationships in complex datasets and translate them into effective fraud detection signals.

• Build reliable, explainable ML solutions: Create models that are robust, interpretable, and evaluated against realistic production conditions.

• Take ideas into production: Work closely with Data Engineering to transform promising ML experiments into scalable, maintainable solutions.

• Drive continuous improvement: Use experimentation, production feedback, and emerging ML techniques to improve model performance and fraud decisioning.

• Make an impact through collaboration: Partner with Product, Fraud Domain Experts, and technical teams to solve complex challenges, share knowledge, and advance ML best practices.

We take pride in enabling career growth in an environment of innovation and teamwork. Our commitment to all Mitekians is to do meaningful work that matters. Our culture is defined by delivering our best to our customers by providing high value solutions and impactful outcomes, by continuously challenging convention, and by caring for each other through collaboration and celebrating our successes. We are committed to creating competitive, equitable compensation & benefits programs and career development opportunities.

Benefit offerings - may vary based on geographic location

Wellness: Universal, supplemental, and private healthcare plan choices based on country specifics

Financial future: retirement/pension plan contributions, MTK stock plan participation

Income protection: life event & disability coverage

Paid time off: generous annual leave, company holidays, volunteer time off

Learning: e-learning license, tuition reimbursement, hackathons

Additional/optional benefits: pet insurance, identity theft protection, legal assistance

We sincerely appreciate your interest in Mitek. We know your time is valuable and look forward to the potential of speaking with you further!

• Design, develop, and evaluate machine learning models for fraud detection, including fraud scoring and ensemble models that combine multiple sources of evidence.

• Analyze large-scale datasets to identify complex patterns, relationships, behaviors, and anomalies, developing new signals to improve fraud detection.

• Define robust experimentation and evaluation methodologies, including model calibration, operating thresholds, and performance metrics under realistic production conditions.

• Develop interpretable and explainable ML approaches that help internal stakeholders and customers understand fraud scores and decisions.

• Explore innovative approaches to fraud decisioning, including human-in-the-loop techniques and causal methods.

• Collaborate with Product and Fraud Domain Experts to translate real-world fraud challenges and production feedback into effective machine learning capabilities.

• Partner with Data Engineering to transform ML prototypes into scalable, maintainable, production-ready solutions.

• Contribute to model monitoring, governance, technical documentation, ML tooling, and shared best practices across the team.

• A Bachelor's, Master's, or PhD degree in Computer Science, Statistics, Mathematics, Machine Learning, or a related quantitative field, or equivalent practical experience.

• Skills and expertise typically gained through 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 in feature engineering and machine learning model development using complex behavioral, relational, or contextual data.

• Strong programming and analytical skills using Python, PySpark, and SQL.

• Experience working with cloud-based machine learning and data infrastructure.