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About This Role
Job Title: Data Science \& Business Intelligence Analyst
Department: Corporate
Reports To: President
Location: Boca Raton, FL *(in\-office position; not remote or hybrid)*
FLSA Status: Exempt
Company Summary
BlueTeam is a US\-based provider of national disaster recovery, remediation, reconstruction, renovation, and roofing services for commercial properties. Our core business focuses on cleanup and mitigation efforts for recovery from fire damage, roof leaks, flooding, pipe bursts, and post\-disaster remediation due to severe weather. We exclusively serve commercial sectors including hospitality, senior housing, healthcare, commercial offices, municipalities, multifamily living, and institutional markets.
SUMMARY:
The Data Science \& Business Intelligence Analyst serves as the company's primary quantitative resource, transforming data from project, financial, and customer systems into decisions leadership can act on. The role spans the full analytical range: data acquisition and modeling, recurring and ad hoc reporting, statistical and predictive modeling, and the applied use of artificial intelligence to extract structured information from the document\-heavy workflows that drive this business.
The analyst will work with structured and unstructured data, build and maintain the reporting layer, develop forecasting and predictive models, validate those models against actual results, and automate manual processes. The standard for this role is defensibility: every number produced must reconcile to its source system, and every model must be documented, tested against data it was not trained on, and explainable to a non\-technical audience. Selecting the simplest method that answers the question is preferred over sophistication for its own sake.
This position supports the executive team crossing all departments of the Company. Initial priorities will center on sales reporting, expanding to enterprise analytics as the reporting foundation matures.
ESSENTIAL DUTIES AND RESPONSIBILITIES:
Data Analytics \& Business Intelligence
- Collect, clean, validate, and transform data from multiple source systems, including project management, accounting, CRM, and field data collection platforms.
- Develop dashboards, reports, and visualizations that provide actionable insights.
- Analyze historical trends, operational performance, productivity metrics, and business outcomes, including job\-level margin, estimate versus actual variance, backlog and pipeline conversion, win rates by client and business unit, and receivable aging and collection cycle time.
- Create recurring and ad hoc reporting for leadership teams.
- Identify patterns, risks, and opportunities through quantitative analysis.
- Build and maintain the queries, extracts, and pipelines that feed the reporting layer, including API\-based extraction from source systems.
- Reconcile reporting to the general ledger and to source systems so that analytical output and financial reporting do not diverge.
Data Science, Statistics \& Predictive Analytics
- Build forecasting models for revenue, backlog conversion, labor and equipment demand, and cash flow, accounting for the seasonality and catastrophe\-driven volatility inherent to storm restoration work.
- Design and interpret experiments, quantify statistical significance, and measure realized business impact against forecast.
- Present quantitative findings with stated confidence, known limitations, and the reasoning behind method selection.
AI, Automation \& Applied Machine Learning
- Apply large language model tooling to production analytical workflows, including structured data extraction from unstructured documents, classification, and summarization, rather than ad hoc manual prompting alone.
- Develop AI\-assisted processes to streamline reporting, research, document review, and decision support, with human review controls at each output stage.
- Evaluate emerging AI capabilities and recommend practical business applications, including build versus buy assessment and cost per unit of output.
- Build automated workflows that reduce manual effort and increase efficiency
Data Management \& Governance
- Ensure data accuracy, integrity, and consistency across reporting systems.
- Support data governance initiatives, validation processes, and data quality improvements.
- Partner with stakeholders to establish reporting standards and best practices.
- Document data sources, transformations, model logic, and code so that all work is reproducible by someone other than the author.
Cross‑Functional Collaboration
- Partner with business leaders to understand strategic priorities and deliver data\-driven recommendations.
- Present findings to both technical and non\-technical audiences.
- Support initiatives across Sales, Operations, Finance, Marketing, and Executive Leadership.
- Translate complex analyses into actionable business recommendations.
- Challenge analytically unsupported conclusions, including those already held by leadership, and state plainly where available data is insufficient to answer the question asked.
QUALIFICATIONS:
- 5 years of progressive experience in data analytics, data science, business intelligence, or a related quantitative field, including hands\-on ownership of both reporting and predictive modeling work.
- Strong analytical and problem\-solving skills with experience interpreting large datasets.
- SQL proficiency sufficient to write and optimize multi\-table joins, aggregations, and window functions against a production database without assistance.
- Working proficiency in Python or R for data manipulation, statistical analysis, and modeling (for example pandas, scikit\-learn, stats models, or equivalent libraries).
- Demonstrated experience building, validating, and putting into use at least one forecasting or predictive model that informed an operating decision.
- Expert proficiency in Excel, including advanced formulas, pivot tables, dynamic financial and operational models, and AI‑assisted model development.
- Proficiency in CRM platforms, with hands‑on experience across multiple systems; ability to navigate, maintain data integrity, and extract insights across different systems environments.
- Proficiency in Power BI, including dashboard design, DAX formulas, and data modeling.
- Working proficiency with current AI tooling applied to real analytical work, including prompt design, structured output, and validation of AI\-generated results before use.
- Excellent communication skills with the ability to translate data into clear insights.
Preferred Qualifications
- Background in construction, restoration, insurance, or another project\-based industry is not required but is a plus.
- Experience building automated dashboards and reporting systems.
- Demonstrated ability to use AI for prospect and client research, including synthesizing information from multiple sources into actionable sales intelligence and executive‑ready presentations.
- Experience with cloud data platforms and pipeline orchestration
- Experience with large language model APIs, retrieval methods, embeddings, and evaluation techniques.
EDUCATION and/or EXPERIENCE:
- Bachelor's degree in statistics, mathematics, economics, data science, computer science, engineering, business analytics, or another quantitative discipline.
PHYSICAL DEMANDS: While performing the duties of this job, the employee is regularly required to type and look at a computer screen for long periods of the day. The employee must be able to sit for long periods of time. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.
QUALIFICATIONS: To perform this job successfully, an individual must be able to perform each essential duty satisfactorily. The requirements listed above are representative of the knowledge, skill, and/or ability required.
NOTE: This job description is not designed to cover or contain a comprehensive listing of activities, duties or responsibilities that are required of the employee for this job. Duties, responsibilities and activities may change at any time with or without notice. BBMK Contracting, LLC dba BlueTeam (BlueTeam) is a Drug Free Workplace as well as an Equal Opportunity Employer. Qualified applicants shall be considered for all positions without regard to race, color, sex, religion, national origin, age, disability, veteran status, or any other status
Salary Context
This $75K-$95K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 2162 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At blueteam, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($85K) sits 60% below the category median. Disclosed range: $75K to $95K.
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.
blueteam AI Hiring
blueteam has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Boca Raton, FL, US. Compensation range: $95K - $95K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
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).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
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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