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About This Role
We are a growing information technology company that offers its employees a culture of success, the chance to work on revolutionary federal IT infrastructure, and the opportunity to grow alongside cutting\-edge technology that is reshaping the industry. We are seeking forward thinking candidates that have strong experience in operational support and can help take to the next level in a pro\-active stance.
Chameleon Integrated Services has expertise in operations management, quality systems, data operations and cybersecurity. We secure some of the most sensitive data for the Department of Defense and for other U. S. federal government agencies. We are known for the great care we take with clients and employees, and we believe in promoting from within.
Senior Data Scientist / AI\-ML \& Anomaly Detection Lead
Position Overview* Position Type: Part\-Time Consultant / Technical Subject Matter Expert
- Target Allocation: 14–18 hours/week average (Note: Workload is highly concentrated around phase deliverables involving rule modernization, anomaly analytics engine build\-out, technical validation, and state acceptance testing).
- Location: Remote (U.S. Based) with periodic travel to Tallahassee, FL as required.
Chameleon is seeking a Senior Data Scientist / AI\-ML \& Anomaly Detection Lead to drive the technical intelligence layer of a high\-visibility contract with the Florida Office of the Chief Inspector General (OCIG). In this role, you will lead the design, enhancement, and optimization of the analytical engines transforming 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. Because this is a high\-visibility, firm\-fixed\-price (FFP) state government contract, you will maintain 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.
Principal Responsibilities* POC Library Modernization: Evaluate, enhance, and modernize the existing 11\-rule baseline POC library to support full enterprise scalability.
- Anomaly Engine Design: Architect transaction\-centric anomaly logic, defining data features, risk scoring models, tolerance thresholds, and alert prioritization criteria.
- Machine Learning Optimization: Develop, refine, and deploy supervised and unsupervised machine learning algorithms where they add measurable validation value over standard deterministic rules.
- Model Explainability \& Traceability: Maintain absolute, non\-black\-box transparency across all algorithms. Ensure every flagged transaction generates clear, human\-readable logic explanations and evidence usable by state auditors or Inspector General investigators.
- Rigorous Validation Testing: Establish comprehensive, audit\-ready validation datasets to measure, verify, and document model accuracy, false\-positive metrics, and rule reproducibility.
- Independent Rerun Support: Provide complete technical documentation, test scripts, and system logs to enable independent OCIG technical validation teams to successfully rerun all anomaly detection routines.
- Drift \& Performance Monitoring: Develop and implement automated pipeline criteria for model scoring transparency, versioning control, feature mapping, and data drift detection.
- Value\-Realization Analytics: Engineer standardized formulas and methodologies to compute quantifiable oversight impacts, including potential cost avoidance, financial recoveries, identified risk exposure, and investigative referrals.
Required Qualifications* Experience Baseline: 10\+ years of comprehensive data analytics and data science experience, with 5\+ years of dedicated, hands\-on machine learning engineering.
- Government Context: Documented history delivering data science, predictive modeling, or advanced analytics solutions within a federal, state, military, or local government framework.
- Core Tech Stack: Advanced, hands\-on mastery of Python and SQL for complex data manipulation and engineering.
- Advanced Analytics Toolkit: Deep expertise across supervised and unsupervised learning, classification, clustering, statistical forecasting, and advanced feature engineering.
- Model Calibration: Proven experience in model evaluation, threshold calibration, and exhaustive false\-positive or false\-negative impact analysis.
- Investigative Translation: Demonstrated ability to translate raw model outputs into defensible, audit\-ready forensic evidence rather than merely outputting an unweighted probability score.
- Scientific Reproducibility: Experience producing comprehensive technical artifacts, configuration baselines, and model documentation sufficient for independent third\-party replication.
- Domain Expertise: Strong experience working with highly disparate, transaction\-level data sets including financial, procurement, contract management, or purchasing card ledger systems.
- Vetting \& Location: 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.
Strong Preferences* 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.
MANDATORY RESUME FORMATTING INSTRUCTIONS
The State of Florida strictly evaluates and verifies all named staff experience for this contract. Generic resumes that only list generalized technical summaries or state they are "familiar with AI/ML" will be automatically rejected.
To be considered for this role, your resume must explicitly detail actual project case studies for your past contract positions, utilizing the following structure:* The Government Customer: Explicitly name the agency and the specific system context (e.g., Treasury, DHS, DLA).
- The Specific Problem: Detail the exact compliance, oversight, or fraud variant the model was designed to detect.
- The Technical Approach: Name the specific datasets, algorithms, models, and toolsets utilized (e.g., Clustering, SAS Enterprise Miner, Python).
- Personal Contribution: Detail exactly what you personally engineered, configured, or deployed regarding features, thresholds, or pipelines.
- Deployment \& Validation Status: State the true production or operational status of the project, including the exact validation method used to prove model accuracy.
- Quantifiable Outcomes: Provide the measurable results achieved, such as false\-positive reduction rates, percentage of pipeline reliability, or volume of cost avoidance unlocked.
*“We are an equal opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, sexual orientation, gender identity, disability or protected veteran status”*
*Texting Privacy Policy*
- Message type: Informational; you will receive text messages regarding your application and potentially regarding interview scheduling.
- No mobile information will be shared with third parties/affiliates for marketing/promotional purposes.
- Message frequency will vary depending on the application process.Msg \& data rates may apply.
- OPT out at any time by texting "Stop".
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Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 4,317 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Chameleon Integrated Services, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $192,890 based on 789 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Chameleon Integrated Services AI Hiring
Chameleon Integrated Services has 2 open AI roles right now. They're hiring across Data Scientist, MLOps Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% of all AI roles offer remote work.
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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