Senior Data Scientist / AI-ML & Anomaly Detection Lead

Chameleon Integrated Services•Tallahassee, FL

17h•Remote

About The Position

Chameleon Integrated Services is seeking a forward-thinking Senior Data Scientist / AI-ML & Anomaly Detection Lead with strong experience in operational support to drive the technical intelligence layer of a high-visibility contract with the Florida Office of the Chief Inspector General (OCIG). This role involves leading the design, enhancement, and optimization of analytical engines to transform a data analytics Proof of Concept (POC) into a secure, enterprise-ready Decision Intelligence Platform. This platform will unify statewide oversight, tracking abnormal spending patterns, contract vulnerabilities, and fraud/waste/abuse risks across up to 35 state agencies. The position requires absolute accountability for achieving legally binding quantitative thresholds, including a 95% or greater rule-output accuracy rate and a 5% or lower false-positive rate, due to the firm-fixed-price nature of the state government contract.

Requirements

10+ years of comprehensive data analytics and data science experience, with 5+ years of dedicated, hands-on machine learning engineering.
Documented history delivering data science, predictive modeling, or advanced analytics solutions within a federal, state, military, or local government framework.
Advanced, hands-on mastery of Python and SQL for complex data manipulation and engineering.
Deep expertise across supervised and unsupervised learning, classification, clustering, statistical forecasting, and advanced feature engineering.
Proven experience in model evaluation, threshold calibration, and exhaustive false-positive or false-negative impact analysis.
Demonstrated ability to translate raw model outputs into defensible, audit-ready forensic evidence rather than merely outputting an unweighted probability score.
Experience producing comprehensive technical artifacts, configuration baselines, and model documentation sufficient for independent third-party replication.
Strong experience working with highly disparate, transaction-level data sets including financial, procurement, contract management, or purchasing card ledger systems.
Must be a U.S.-based citizen or resident.
Must be able to successfully clear an FDLE Level II background screening (including fingerprinting) within 5 business days of contract award.

Nice To Haves

Prior experience engineering fraud, waste, abuse, improper payment, financial crime, or corporate risk analytics models.
Working context with data from oversight agencies such as Treasury, FinCEN, CMS, Census, or state-level Medicaid and revenue departments.
Hands-on production experience utilizing Azure Databricks, MLflow, or Azure Machine Learning within secure Government Cloud environments.
Deep familiarity with building hybrid detection architectures that seamlessly blend deterministic business rule repositories with machine learning anomaly detection.
Prior usage of formal model cards, explainability packages, and structured human-in-the-loop validation review workflows.

Responsibilities

Evaluate, enhance, and modernize the existing 11-rule baseline POC library to support full enterprise scalability.
Architect transaction-centric anomaly logic, defining data features, risk scoring models, tolerance thresholds, and alert prioritization criteria.
Develop, refine, and deploy supervised and unsupervised machine learning algorithms where they add measurable validation value over standard deterministic rules.
Maintain absolute, non-black-box transparency across all algorithms, ensuring every flagged transaction generates clear, human-readable logic explanations and evidence usable by state auditors or Inspector General investigators.
Establish comprehensive, audit-ready validation datasets to measure, verify, and document model accuracy, false-positive metrics, and rule reproducibility.
Provide complete technical documentation, test scripts, and system logs to enable independent OCIG technical validation teams to successfully rerun all anomaly detection routines.
Develop and implement automated pipeline criteria for model scoring transparency, versioning control, feature mapping, and data drift detection.
Engineer standardized formulas and methodologies to compute quantifiable oversight impacts, including potential cost avoidance, financial recoveries, identified risk exposure, and investigative referrals.