Act as an advisor to leadership to develop or influence applications, network, information security, database, operating systems, or web technologies for highly complex business and technical needs across multiple groups
Lead the strategy and resolution of highly complex and unique challenges requiring in-depth evaluation across multiple areas or the enterprise, delivering solutions that are long-term, large-scale and require vision, creativity, innovation, advanced analytical and inductive thinking
Translate advanced technology experience, an in-depth knowledge of the organizations tactical and strategic business objectives, the enterprise technological environment, the organization structure, and strategic technological opportunities and requirements into technical engineering solutions
Provide vision, direction and expertise to leadership on implementing innovative and significant business solutions
Maintain knowledge of industry best practices and new technologies and recommends innovations that enhance operations or provide a competitive advantage to the organization
Strategically engage with all levels of professionals and managers across the enterprise and serve as an expert advisor to leadership
Research, design, develop, and productionize machine learning models for fraud detection (supervised, unsupervised, semi-supervised), anomaly detection, behavioral biometrics, network intrusion detection, account takeover prevention, and synthetic identity fraud.
Build and maintain real-time and near-real-time scoring pipelines that deliver sub-second fraud/attack predictions during payment authorization, login, and high-risk interactions.
Perform advanced feature engineering on complex, heterogeneous data sources (transactional, temporal, graph-based, textual threat intel, device & behavioral signals) to create high-signal features for model training and inference.
Apply techniques such as graph neural networks, sequence modeling (LSTM/Transformer), ensemble methods, autoencoders, isolation forests, contrastive learning, and adversarial robustness to address evolving fraud and cyber threats.
Conduct rigorous model evaluation, explainability analysis (SHAP, LIME, counterfactuals), bias/fairness checks, and performance monitoring in production environments.
Partner closely with data engineers to define requirements for feature stores, real-time feature pipelines, and model-serving infrastructure.
Collaborate with fraud investigators, threat hunters, SOC analysts, AML teams, and product owners to translate business problems into modeling objectives and iteratively improve detection effectiveness while minimizing false positives.
Contribute to model risk management processes, model documentation, validation, and regulatory reporting (SR 11-7 / OCC guidelines, model risk frameworks).
Stay current with state-of-the-art research in adversarial ML, fraud/cybersecurity analytics, federated learning, privacy-preserving ML, and explainable AI in high-stakes domains.
Participate in model experimentation sprints, A/B testing of detection strategies, and red-team exercises simulating sophisticated attacks.

Requirements

7+ years of Engineering experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education.
Strong proficiency in Python (pandas, scikit-learn, XGBoost/LightGBM/CatBoost, PyTorch/TensorFlow, PySpark) and experience with ML experimentation frameworks (MLflow, Weights & Biases, etc.).
Deep understanding of supervised & unsupervised learning, imbalanced classification, anomaly/outlier detection, time-series analysis, and ensemble techniques.
Hands-on experience deploying models into real-time production environments (e.g., via APIs, Kafka consumers, Spark Streaming, or low-latency serving platforms).
Solid SQL skills and comfort working with large-scale data warehouses/lakehouses (Snowflake, Databricks, BigQuery).
Proven track record of delivering measurable business impact (e.g., fraud loss reduction, false-positive rate improvement, detection rate lift) in regulated environments.
Experience with graph-based modeling (GraphSAGE, GNNs, link prediction) for fraud rings, money laundering networks, or lateral movement detection.
Master's or Ph.D. in Computer Science, Statistics, Machine Learning, Data Science, Applied Mathematics, or related quantitative discipline (or equivalent demonstrated experience).
Familiarity with adversarial ML, model robustness, concept drift detection, and active learning in security contexts.
Background in privacy-preserving techniques (differential privacy, federated learning, secure multi-party computation) or synthetic data generation for security use cases.
Exposure to financial crime domains: card-present/card-not-present fraud, ACH/wire fraud, mule accounts, trade-based money laundering, BEC, ransomware payments.
Knowledge of financial regulatory model risk frameworks and experience with model validation/documentation.
Publications, Kaggle rankings, or contributions to open-source ML/security projects are a plus.

Benefits

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Applicant Tracking System Keywords

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Hard Skills & Tools

Pythonmachine learningsupervised learningunsupervised learninganomaly detectionfeature engineeringgraph neural networksSQLmodel evaluationreal-time production environments

Soft Skills

leadershipstrategic thinkinganalytical thinkingcommunicationcollaborationproblem-solvinginnovationcreativityvisionexpert advisory

Certifications

Master's in Computer SciencePh.D. in Machine LearningPh.D. in Data SciencePh.D. in Applied Mathematics