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Fraud Detection Machine Learning Jobs in Missouri

Jobgether

$97K - $128K/yr

... machine-learning models, and evidence workflows. * Deep knowledge of authentication and ... Strong commercial awareness and an understanding of how fraud, security, customer experience, and ...

Jobgether

$100K - $120K/yr

Advanced Python development skills and practical experience with deep learning and machine learning techniques. * Experience with NLP, forecasting, classification, regression, and anomaly detection.

Yuno

... enabling fraud detection, revenue analytics, payment optimization, and data-driven product ... Collaborate closely with Product, Analytics, Machine Learning, Finance, and Compliance teams in an ...

Tenex

Threat Detection Engineer

Kansas City, MO · On-site

$120 - $180/hr

About Tenex TENEX is an AI-native, automation-first, built-for-scale Managed Detection and Response ... Knowledge of data science and machine learning concepts as applied to security analytics. Why Join ...

BioCatch

$114.90 - $149.37/hr

BioCatch is the leader in Behavioral Biometrics, a technology that leverages machine learning to ... fraud, facilitate digital transformation, and grow customer relationships.. BioCatch's Client ...

New

Bass Pro Shops

Lead Engineer AI/ML - Onsite

Springfield, MO · On-site

$93K - $122K/yr

The Machine Learning Engineer designs, builds, tests, and optimizes machine learning systems that ... detection, multimodal AI, or generative AI. * Experience building production-quality APIs, services ...

Third-Party Job Posts

As a Staff Machine Learning Engineer , you will play a key role in building and implementing ... drift detection at scale. * Software Engineering Rigor: Strong background in Python, distributed ...

TAMKO

Manufacturing Data Scientist

Joplin, MO · On-site

Develop and deploy machine learning, predictive analytics, and prescriptive analytics, including time-series anomaly detection, predictive maintenance, soft sensors, forecasting, and Digital Twins.

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Fraud Detection Machine Learning information

What is fraud detection using machine learning?

Fraud detection using machine learning involves leveraging algorithms and data analysis techniques to identify suspicious or fraudulent activities in various domains, such as banking, e-commerce, or insurance. These systems analyze large volumes of transaction data to detect patterns or anomalies that may indicate fraud. Machine learning models can adapt over time, improving their accuracy as they are exposed to more data. This approach helps organizations automate and enhance their ability to prevent, detect, and respond to fraudulent behavior efficiently.

What are some common challenges faced by professionals working in fraud detection machine learning, and how can they be addressed?

Professionals in Fraud Detection Machine Learning often face challenges such as dealing with highly imbalanced datasets, rapidly evolving fraud patterns, and the need for real-time detection. Managing data imbalance requires careful selection of evaluation metrics and specialized algorithms. Staying ahead of new fraud tactics involves continuous model retraining and close collaboration with domain experts. Additionally, integrating machine learning solutions with existing systems often requires cross-functional teamwork with IT, security, and compliance teams.

What are the key skills and qualifications needed to thrive as a fraud detection machine learning specialist, and why are they important?

To thrive as a Fraud Detection Machine Learning Specialist, you need strong expertise in machine learning, statistical analysis, and programming languages like Python or R, typically supported by a degree in computer science, data science, or a related field. Familiarity with tools such as TensorFlow, Scikit-learn, SQL databases, and experience with big data platforms or cloud services is highly valuable. Critical thinking, attention to detail, and effective communication are crucial soft skills for identifying complex fraud patterns and collaborating with interdisciplinary teams. These competencies are vital for developing accurate models that protect organizations from financial losses and maintain trust with customers.

What is the difference between Fraud Detection Machine Learning vs Fraud Analyst?

AspectFraud Detection Machine LearningFraud Analyst
CredentialsData science, machine learning certifications, programming skillsFinance, criminal justice degrees, analytical skills
Work EnvironmentData-driven, tech-focused, often in financial or e-commerce sectorsInvestigative, report-focused, in financial institutions or insurance companies
Employer & IndustryTech companies, banks, e-commerce platformsFinancial institutions, insurance firms, retail

Fraud Detection Machine Learning involves developing algorithms to identify fraudulent activities automatically, relying heavily on data analysis and programming. Fraud Analysts manually investigate suspicious cases and interpret data insights. While both roles aim to prevent fraud, Machine Learning specialists focus on building models, whereas Fraud Analysts focus on case investigation and decision-making.

What are popular job titles related to Fraud Detection Machine Learning jobs in Missouri?

For Fraud Detection Machine Learning jobs in Missouri, the most frequently searched job titles are:

What job categories do people searching Fraud Detection Machine Learning jobs in Missouri look for?

The top searched job categories for Fraud Detection Machine Learning jobs in Missouri are:

What cities in Missouri are hiring for Fraud Detection Machine Learning jobs?

Cities in Missouri with the most Fraud Detection Machine Learning job openings:

Fraud Detection Machine Learning jobs near you
Infographic showing various Fraud Detection Machine Learning job openings in Missouri as of August 2026, with employment types broken down into 1% As Needed, 73% Full Time, 23% Part Time, 1% Temporary, and 2% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution.

Senior Product Manager (Risk & Fraud)

Jobgether

On-site

$97K - $128K/yr

Full-time

Medical, PTO

This job post has expired 1 day ago. Applications are no longer accepted.

Job description

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior Product Manager (Risk & Fraud) based in Netherlands.

As a Senior Product Manager, you will own the strategy and roadmap for fraud prevention, risk management, account security, and trusted customer experiences.
You will shape solutions across account creation, authentication, checkout, payments, subscriptions, and post-purchase journeys in a high-volume transactional environment.
The role combines product strategy, data-driven decision-making, and hands-on collaboration with engineering, data, finance, risk operations, and support teams.
You will balance conversion, fraud losses, customer experience, compliance, and operational costs to maximize business impact.
Your work will directly influence fraud rates, chargebacks, dispute outcomes, false positives, and overall platform profitability.
You will lead initiatives from discovery through launch and optimization while staying ahead of evolving fraud tactics, payment technologies, and regulatory requirements.
This is an opportunity to take significant ownership in a global, distributed product organization and build safer, more trusted commerce experiences.

Accountabilities:
  • Own and evolve the product roadmap for account management, authentication, authorization, security, fraud prevention, and KYC/KYB capabilities.
  • Lead initiatives covering MFA, reCAPTCHA, OAuth2, SSO, SAML, enterprise identity integrations, role-based access control, subscriptions, entitlements, usage limits, and account security.
  • Define strategies for payment security across checkout, fraud rules, 3-D Secure, and Strong Customer Authentication (SCA).
  • Analyze end-to-end customer journeys, including onboarding, login, account recovery, subscription changes, white-label and gray-label experiences, identifying opportunities to reduce friction while strengthening fraud protection.
  • Define and prioritize product initiatives that balance conversion, fraud exposure, compliance requirements, customer experience, and operational efficiency.
  • Use ROI-driven prioritization and trade-off analysis to optimize business outcomes and take ownership of the financial performance of fraud and security initiatives.
  • Lead product delivery from discovery and problem definition through release and post-launch optimization, establishing measurable success criteria and monitoring performance.
  • Track key metrics including fraud rates, chargeback ratios, dispute win rates, false-positive rates, manual review times, and fraud-related support volumes.
  • Partner closely with engineering, data, finance, risk operations, customer support, logistics, and other cross-functional teams to deliver resilient and compliant product experiences.
  • Manage relationships and integrations with payment service providers, identity providers, and relevant technology partners.
  • Monitor market developments, fraud signals, payment capabilities, SCA/PCI/AML trends, and competitor strategies, translating relevant insights into experiments and roadmap initiatives.
  • Communicate product strategy, priorities, trade-offs, and outcomes clearly across distributed global teams.
Requirements:
  • 6+ years of experience working in fraud and risk, with hands-on ownership of fraud prevention for transactional, marketplace, payments, eCommerce, or other high-volume products.
  • Strong understanding of fraud patterns and prevention techniques, including account takeover, synthetic and friendly fraud, triangulation, chargebacks, disputes, device and behavioral signals, rules engines, machine-learning models, and evidence workflows.
  • Deep knowledge of authentication and authorization concepts, including MFA, SSO, OAuth2, enterprise identity integrations, and role-based access control.
  • Strong product management experience, including roadmap ownership, problem framing, prioritization, experimentation, and end-to-end product delivery.
  • Highly data-driven approach, with the ability to work confidently with funnels, KPIs, experimentation frameworks, ROI models, and trade-offs between conversion, fraud losses, and operational costs.
  • Demonstrated ability to influence engineering, data, finance, operations, and other stakeholders across distributed international teams.
  • Excellent written and verbal communication skills, with the ability to translate complex risk and technical concepts into clear product decisions.
  • Highly organized, resourceful, pragmatic, and entrepreneurial, with a strong sense of ownership and the ability to operate effectively in a fast-moving environment.
  • Experience with PCI compliance or KYC/KYB workflows is a valuable advantage.
  • Strong commercial awareness and an understanding of how fraud, security, customer experience, and profitability interact.
Benefits:
  • Competitive salary and an equity package, providing an opportunity to participate in the company's growth.
  • Gym and wellness reimbursement to support physical health and wellbeing.
  • Generous vacation policy that encourages employees to take meaningful time away from work.
  • Duvet Days for additional flexibility when you need a low-key day to recharge.
  • Dedicated Mental Health Days to support wellbeing and personal recovery.
  • Four weeks of Work from Anywhere each year, providing additional flexibility to work from another location.
  • Professional development support to help you build skills and progress toward your career goals.
  • Fully remote working environment with the opportunity to collaborate with a globally distributed team.
  • Exposure to international product, payments, fraud, risk, and eCommerce challenges across multiple markets.
How Jobgether works:
We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.
We appreciate your interest and wish you the best!
 Why Apply Through Jobgether? 
 
Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.
 
 
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We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
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