Machine Learning Scientist

miteksystems-2; United States; Hybrid; USD 120,000 - 170,000; Full time

Benefits offered by this job

Healthcare options

Retirement contributions

Stock plan participation

Paid leave

Learning support

Home-office allowance

Pet insurance (optional)

Job summary

miteksystems-2 seeks a skilled ML professional to design and evaluate fraud detection models that explain decisions and adapt to changing patterns. You will analyze massive datasets, define experiments, and work with product and data teams to bring robust detection to production.

You will build interpretable scoring methods, explore signals, calibrate thresholds, and contribute to monitoring and governance. This role emphasizes collaboration and practical impact.

Qualifications

• Strong ML fundamentals and hands-on experience building and evaluating models.
• Ability to analyze large-scale data and translate findings into improvements.
• Experience designing robust experiments and evaluating model performance.
• Excellent communication with product and technical partners.

Responsibilities

• Design and evaluate fraud detection models, including scoring approaches that combine multiple evidence sources.
• Explore large datasets to find behavioral patterns, relationships, anomalies, and signals that can improve detection.
• Define rigorous experiments and evaluation methods, including calibration, decision thresholds, and production-relevant performance measures.
• Build interpretable approaches that help stakeholders understand model scores and decisions.
• Investigate human-in-the-loop and causal methods for fraud decisioning.
• Work with product, domain experts, and data engineering to operationalize models and improve them using production feedback.
• Contribute to monitoring, governance, documentation, shared tooling, and team practices.

Skills

Machine learning fundamentals

Data analysis at scale

Experiment design

Model interpretation

Communication with product partners

Education

Bachelor's degree in CS/Math/Stats

Tools

Python

Scikit-learn

TensorFlow/PyTorch

Job description

Role overview

Develop machine learning capabilities that help detect fraud and make risk decisions more accurate and understandable. The work spans analysis of large datasets, model design and evaluation, and close partnership with product, fraud specialists, and data engineering to bring useful approaches into production.

Responsibilities

• Design and evaluate fraud detection models, including scoring approaches that combine multiple evidence sources.
• Explore large datasets to find behavioral patterns, relationships, anomalies, and signals that can improve detection.
• Define rigorous experiments and evaluation methods, including calibration, decision thresholds, and production-relevant performance measures.
• Build interpretable approaches that help stakeholders understand model scores and decisions.
• Investigate human-in-the-loop and causal methods for fraud decisioning.
• Work with product, domain experts, and data engineering to operationalize models and improve them using production feedback.
• Contribute to monitoring, governance, documentation, shared tooling, and team practices.

Requirements

• Degree in computer science, statistics, mathematics, machine learning, or a related quantitative discipline, or equivalent practical experience.
• Strong machine learning fundamentals and hands-on experience developing and evaluating models.
• Ability to analyze complex, large-scale data and translate findings into practical detection improvements.
• Experience designing robust experiments and assessing model performance in realistic conditions.
• Ability to communicate model behavior and collaborate with product and technical partners.

Benefits and work setup

The role offers remote or office-based work with flexibility. Listed compensation is €50,000–€70,000 base, plus a 10% annual bonus and equity.

• Benefits may vary by location and include healthcare options, retirement contributions, stock plan participation, paid leave, learning support, a home-office allowance, and optional coverage such as pet insurance.