Sr Analytics Engineer, Applied AI

Job Description

Flybridge Staffing is currently seeking a Senior Analytics Engineer, Applied AI, to join a reputable organization in the Boca Raton Area. (Hybrid onsite preferred)

*Flexible Salary, corporate bonus, and amazing benefits.
****NO SPONSORSHIP AVAILABLE**** US Citizen, GC, only, please.

Position Summary:

The analytics engineer role is evolving to a practitioner who harnesses AI-powered tools and platforms to solve a broader range of problems, at greater speed, with higher quality, and across more of the business than ever before. Our Analytics team is looking for a Senior Analytics Engineer who embodies this evolution.

You'll bring a strong statistical and machine learning foundation, but what sets you apart is how you apply it: leveraging AI platforms, generative AI, automated ML, coding copilots, and emerging tools to compress development cycles, explore previously unfeasible problems, and deliver intelligence across the full breadth of what our Analytics team supports.

This is a hands-on, high-impact role within a centralized Analytics team that works at the center of the business.

Required Qualifications:

• Bachelor's or master's degree in computer science, data science, Statistics, Applied Mathematics, or a related quantitative field.
• 6+ years of professional experience in applied analytics, or a hybrid data science/engineering role.
• Demonstrated ability and enthusiasm for adopting new AI tools, platforms, and workflows to improve speed and quality of analytical work.
• Strong foundation in statistical methods and machine learning techniques: regression, classification, clustering, time-series analysis, hypothesis testing, and experimental design.
• Proficiency in Python (pandas, NumPy, scikit-learn, matplotlib/seaborn) and SQL for data manipulation, analysis, and modeling.
• Hands-on experience with cloud-based analytics platforms-preferably Azure Databricks-including notebook-based development and working with large-scale data.
• Experience building data pipelines or feature engineering workflows that support model development (not just consuming pre-built datasets).

Preferred Qualifications:

• Experience using generative AI tools (LLMs, AI coding assistants, AI-powered analytics platforms) as part of a professional analytics workflow-not just experimentation.
• Experience with Claude Enterprise.
• Familiarity with PySpark and distributed computing for large-scale data transformation and feature engineering.
• Working knowledge of Salesforce data structures, CRM analytics, or marketing/sales funnel analysis.
• Exposure to Power BI or similar visualization tools from a data integration or model-output delivery perspective.
• Experience with healthcare data, HIPAA-regulated environments, or member/patient-level analytics.
• Background in NLP, text analytics, or working with unstructured data sources.
• Analytics engineer who has embraced the AI era-you don't just know how to build a model from scratch, you know when to let an AI tool do it faster and focus your energy on the problem framing, validation, and business impact.
• Equally comfortable writing a PySpark transformation, training a classification model, prompting an LLM for document extraction, and presenting findings to the Executive.

Key Responsibilities

Solve Problems Across Business Using AI-Augmented Analytics

• Develop predictive models, scoring engines, segmentation frameworks, and forecasting solutions that address real business needs-member retention, physician conversion, sales pipeline optimization, pricing analysis, and more.
• Use AI platforms and tools to dramatically accelerate the analytics lifecycle: automated feature engineering, AI-assisted EDA, model selection, and rapid prototyping that would have taken weeks using traditional approaches.
• Design analytical frameworks that translate ambiguous business questions into testable hypotheses and measurable outcomes.
• Deliver model outputs in formats the business can act on-integrated into Power BI dashboards, embedded in operational workflows, or surfaced as actionable recommendations to leadership.

Explore, Evaluate & Adopt Emerging AI Tools and Platforms

• Stay on the leading edge of the AI tooling landscape-continuously scouting, testing, and integrating new platforms, APIs, and workflows that make the team faster and smarter.
• Evaluate generative AI capabilities (large language models, multimodal models, AI agents) for practical application to MDVIP's business problems, separating genuine value from hype.
• Build proof-of-concept solutions that demonstrate the art of the possible: AI-assisted document analysis, intelligent search over unstructured data, natural language interfaces to analytics, automated quality assurance, and other use cases that expand what the Analytics team can deliver.
• Establish practical guidelines for when and how to use AI tools effectively-helping the broader team adopt these capabilities with confidence and good judgment.

Engineer the Data That Powers the Analysis

• Build and maintain the data pipelines, feature tables, and curated datasets that feed your own models and the team's analytical work-using Databricks, SQL, and Python.
• Work across MDVIP's data ecosystem-Salesforce, SQL Server, Snowflake, third-party sources-to source, transform, and integrate the data needed for analysis.
• Apply data quality discipline: validation checks, anomaly detection, and documentation that ensure your analytical outputs are trustworthy.
• Think like an engineer when building analytical assets-write clean, version-controlled, reusable code that others on the team can understand and extend.

Collaborate, Communicate & Elevate the Team

• Partner with analysts, BI developers, data engineers, and business stakeholders to understand requirements, shape solutions, and deliver results iteratively.
• Translate complex analytical findings and model outputs into clear, compelling narratives for non-technical audiences, including senior leadership.
• Share what you learn-new tools, techniques, and approaches-with the team through documentation, demos, and hands-on knowledge transfer.
• Contribute to a culture of pragmatic experimentation where the team is constantly learning, testing new approaches, and raising the bar on what Analytics delivers to the business.

If your background aligns with the above details and you would like to learn more, please send your resume to View email address on click.appcast.io or on our website, and one of our recruiters will be in touch with you ASAP.

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