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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in building and deploying machine learning models for fraud detection, including the ability to create labeled datasets and evaluate model efficacy. Proficient in collaborating with cross-functional teams to enhance feature pipelines and ensure model performance in production environments.
Highest-signal resume keywords
Machine Learning Model DeploymentFraud Detection ExperiencePython ProgrammingSQL ProficiencyCloud ML Platform Knowledge
ATS Keywords
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Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Machine LearningAnomaly DetectionFraud DetectionRisk ScoringFeature EngineeringModel MonitoringData LabelingEvaluation MethodologyLow-Latency Model ServingUnsupervised Detection Methods
Soft Skills
Problem-SolvingCollaborationCommunication
Tools & Technologies
RedisKafkaAirflowAWS SageMakerKubeflowKServe
Industry Keywords
Ad FraudMobile Ad TechApp MonetizationInstall Attribution FraudIncentivized Advertising
Tech Stack
Tools & technologiesAirflowAWSCloudKafkaKubernetesPythonRedisSQL
About the role
Key responsibilities & impact- Take real-time anomaly detection and device risk models from development into production so their output drives payout and blocking decisions
- Create and maintain a golden labeled fraud dataset
- Define evaluation methodology and baseline fraud-efficacy metrics for judging models and rule changes
- Design and evolve streaming and batch features feeding the Redis online feature store
- Partner with data engineering on feature freshness, correctness, and lineage
- Plan and drive evolution of the training and serving stack, including evaluating migration from SageMaker to Kubeflow and KServe
- Address latency on the real-time scoring path
- Investigate fraud spikes and analyze user and device behavior
- Turn findings into new detection signals, features, and models
- Monitor model and feature health in production
- Write runbooks and data lineage documentation
- Participate in on-call for the fraud detection stack
- Collaborate with data engineering, backend, and product partners
Requirements
What you’ll need- Several years building ML models and taking them all the way to production, including deployment, monitoring, and iteration after launch
- Hands-on experience with fraud detection, abuse prevention, risk scoring, or anomaly detection, including working with sparse, noisy, or adversarial labels
- Experience with low-latency model serving and online/offline feature pipelines, such as Redis or another online feature store, Kafka or similar streaming, and Airflow
- Ability to build labeled datasets from imperfect signals and design evaluation methods that hold up when fraud patterns shift
- Strong Python and SQL
- Working knowledge of a cloud ML platform such as AWS SageMaker, Kubeflow, or KServe
- Ability to take ambiguous problems to shipped outcomes with limited oversight, write things down, and work well with backend, data, and product partners
- Use of AI coding tools
- English sufficient for professional verbal and written exchanges
- Experience with ad fraud, install/attribution fraud, or fraud in rewarded or incentivized advertising is an additional qualification
- Experience with unsupervised or semi-supervised detection methods such as autoencoders, isolation forests, or graph-based approaches is an additional qualification
- Experience migrating ML serving between platforms or building MLOps tooling on Kubernetes is an additional qualification
- Background in mobile ad tech, mobile games, or app monetization is an additional qualification
Benefits
Comp & perks- Comprehensive health, life, and disability insurance
- Commute subsidy
- Employee stock ownership
- Competitive retirement/pension plans
- Generous vacation and personal days
- Support for new parents through leave and family-care programs
- Office food snacks
- Mental Health and Wellbeing programs and support
- Employee Resource Groups
- Global Employee Assistance Program
- Training and development programs
- Volunteering and donation matching program
- Equity awards
- Participation in company incentive plans, such as annual discretionary bonuses or sales commissions