Max Voelker

I lead a machine learning team building production fraud prevention systems at AWS. Over 9 years in ML and 7 years in management at AWS and Fidelity, I've focused on developing teams, defining technical strategy, and delivering measurable business impact. I define multi-year ML strategy, guide technical direction for systems processing billions of daily events, and partner with engineering and product teams across AWS's service portfolio. At Fidelity, I pioneered their first ML-powered financial crimes models in a highly regulated environment and hold a patent for anomaly detection (US 10,826,927).

AWS Fraud Prevention Platform

Lead team building fraud prevention systems across 300+ AWS services processing billions of daily events with $60M+ annual business impact.

Generative AI Detection System

Led cross-org ML initiative enabling safe scaling of GenAI services (Bedrock, SageMaker, Kiro) to millions of customers, preventing $20.8M in fraud losses while maintaining seamless customer experience.

Enterprise Risk Management Platform

Built risk management platform for $32B portfolio at Fidelity with significantly reduced false positives while achieving regulatory compliance through scalable ML infrastructure.

Data Exfiltration Detection Patent

Invented and patented systems and methods for detecting anomalous data traffic over proxy servers using isolation forest algorithms and risk-based feature extraction. US Patent 10,826,927.

Amazon Web Services (AWS)

Data Science Manager, AWS Fraud Prevention | 2023 - Present Lead distributed ML team building fraud prevention systems across 300+ AWS services, processing billions of daily events with $60M+ annual business impact. Define multi-year ML strategy and technical roadmap, partnering with engineering and product teams globally. Scaled team from 4 to 8 members (88% hire rate), developed 3 to promotion (60% rate). Led cross-org initiative enabling safe scaling of GenAI services (Bedrock, SageMaker, Kiro), preventing $20.8M in fraud losses.

Amazon Web Services (AWS)

Data Scientist, AWS Fraud Prevention | 2021 - 2023 Built production ML systems processing 1.3B+ daily events, achieving $60M+ in annual cost savings and 97% fraud reduction. Owned full ML lifecycle from research through deployment, including real-time inference architecture, automated retraining pipelines, and model monitoring at billion-event scale. Implemented gradient boosting models for high-throughput classification and developed transformer-based sequence models improving prediction accuracy 35%.

Fidelity Investments

Senior Manager, Data Science | 2018 - 2021 Led 4-person ML team in Enterprise Risk Management delivering 10+ production systems across classification, anomaly detection, and risk forecasting. Launched Fidelity's first ML-powered financial crimes detection models in highly regulated environment, partnering with compliance, legal, and risk management. Built market risk forecasting system for $32B institutional margin book using LSTMs and Monte Carlo simulation. Deployed ML infrastructure processing 200M+ daily transactions with sub-second latency.

Fidelity Investments

Manager, Analytics & Insights | 2016 - 2018 Led cross-functional team of 4 scientists, analysts, and engineers building analytics and ML capabilities across Corporate Risk and Compliance programs. Developed and patented ML system for anomaly detection using isolation forests, reducing incidents 98% across $2.8T in assets (US Patent 10,826,927). Built analytics program standardizing data-driven decision making and established big data monitoring capabilities managing billions of records.

Machine Learning

Deep Learning, Neural Networks, Transformers & LLMs (GPT, BERT), PyTorch, TensorFlow, Anomaly Detection, Fraud Detection, Gradient Boosting (XGBoost, LightGBM), Feature Engineering, Time Series Analysis

Leadership & Management

Team Building & Scaling, Hiring & Talent Development, Technical Strategy & Roadmapping, Performance Management, Career Development, Cross-functional Leadership, Stakeholder Management

MLOps & Infrastructure

SageMaker, MLflow, Real-time Inference, Model Deployment, CI/CD Pipelines, Model Monitoring, A/B Testing, Feature Stores, Model Versioning, Distributed Systems

Programming & Cloud

Python, R, SQL, Docker, Kubernetes, AWS (EC2, S3, Lambda, SageMaker, EMR, Bedrock), Spark, Hadoop, Kafka, Airflow

New York University, Leonard N. Stern School of Business

Master of Business Administration Specializations: Business Analytics, Quantitative Finance, Financial Technology

Northeastern University, D'Amore-McKim School of Business

Bachelor of Science in Business Administration Specializations: Information Systems, Marketing