Senior Data Scientist / AI-ML & Anomaly Detection Lead

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior Data Scientist / AI-ML & Anomaly Detection Lead based in United States.

This role leads the technical intelligence layer of a high-visibility government data and analytics initiative. You will design and optimize anomaly detection engines that identify unusual spending, contract vulnerabilities, and fraud, waste, and abuse risks. The position combines advanced data science, machine learning engineering, statistical analysis, and rigorous model validation. You will work with complex, transaction-level datasets and build transparent, explainable analytical solutions suitable for auditors and investigators. A major focus will be on achieving measurable accuracy, controlling false positives, and ensuring every model can be independently reproduced and validated. The role is highly hands-on, with significant ownership over model architecture, feature engineering, thresholds, monitoring, and technical documentation. This is a part-time consulting engagement, averaging 14–18 hours per week, with workload concentrated around key delivery and acceptance milestones.

Accountabilities:: Lead the modernization of an existing 11-rule proof-of-concept library, evolving deterministic analytics into a scalable enterprise detection framework.

Architect transaction-focused anomaly detection approaches, defining relevant data features, risk scores, tolerance thresholds, alert priorities, and detection criteria.

Develop, refine, and deploy supervised and unsupervised machine learning models where they provide measurable value beyond conventional business rules.

Apply advanced statistical and machine learning techniques including classification, clustering, forecasting, feature engineering, and anomaly detection.

Establish rigorous model evaluation and calibration processes, including accuracy measurement, threshold optimization, and detailed false-positive and false-negative analysis.

Ensure all analytical models remain explainable and transparent, with human-readable reasoning and supporting evidence for every material anomaly or flagged transaction.

Build audit-ready validation datasets and technical test processes capable of demonstrating model accuracy, reproducibility, and compliance with defined performance thresholds.

Provide technical documentation, configuration baselines, test scripts, and system logs that enable independent government validation teams to reproduce and rerun analytical routines.

Implement monitoring approaches for model scoring, feature mapping, version control, data drift, and ongoing analytical performance.

Develop standardized methodologies for measuring the value generated by analytical insights, including potential cost avoidance, financial recovery, risk exposure, and investigative referrals.

Work with complex financial, procurement, contract, purchasing-card, and other transaction-level datasets to uncover patterns and potential risks.

Maintain a strong focus on scientific reproducibility, technical traceability, and defensible analytical evidence throughout the project lifecycle.

Support technical validation, milestone acceptance, and performance tuning activities as required, including periodic travel to Tallahassee, Florida.

Requirements:

10+ years of comprehensive experience in data analytics and data science, including at least 5 years of hands-on machine learning engineering experience.

Demonstrated experience delivering data science, predictive modeling, or advanced analytics solutions within federal, state, military, or local government environments.

Advanced hands-on proficiency with Python and SQL for complex data manipulation, analysis, engineering, and model development.

Deep expertise in supervised and unsupervised machine learning, classification, clustering, statistical forecasting, anomaly detection, and advanced feature engineering.

Proven ability to evaluate and calibrate models, establish appropriate thresholds, and analyze the operational impact of false positives and false negatives.

Experience translating model outputs into defensible, audit-ready evidence rather than relying solely on probability scores or opaque predictions.

Strong understanding of scientific reproducibility, including the creation of technical documentation, configuration baselines, validation artifacts, and materials that support independent third-party replication.

Experience working with highly diverse, transaction-level datasets, particularly financial, procurement, contract management, purchasing-card, or related ledger data.

Prior experience developing fraud, waste, abuse, improper-payment, financial-crime, or corporate-risk analytics is strongly preferred.

Experience working with oversight-oriented data from government agencies or comparable public-sector environments is a plus.

Hands-on experience with Azure Databricks, MLflow, or Azure Machine Learning in secure government cloud environments is preferred.

Familiarity with hybrid detection architectures combining deterministic business rules with machine learning-based anomaly detection is highly desirable.

Experience with model cards, explainability frameworks, and structured human-in-the-loop validation workflows is a strong advantage.

Must be a U.S.-based citizen or resident and able to successfully complete an FDLE Level II background screening, including fingerprinting, within 5 business days of contract award .

Candidates should be prepared to demonstrate specific government project experience, including the agency served, analytical problem addressed, technical approach, individual contribution, deployment and validation status, and measurable outcomes.

Benefits:

Part-time consulting engagement averaging 14–18 hours per week .

Remote work within the United States.

Flexible workload structure, with additional hours concentrated around major project milestones, technical validation, and government acceptance activities.

Milestone- and deliverable-based fixed-price compensation structure.

Opportunity to contribute to a high-visibility government Decision Intelligence initiative addressing financial oversight, risk detection, and fraud, waste, and abuse.

Exposure to advanced machine learning, anomaly detection, data engineering, and explainable AI applications in a complex public-sector environment.

Periodic travel to Tallahassee, Florida , as required by project activities.

Opportunity to work on a technically demanding initiative involving large-scale, multi-agency data and measurable analytical performance requirements.

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.     #LI-CL1