How many Science & Research jobs are open in Tampa, FL right now?

There are currently 17 open science & research positions in Tampa, FL listed on Clera. New openings are added daily as companies post roles.

Which companies are hiring for Science & Research roles in Tampa, FL?

Companies currently hiring include University of South Florida - School of Social Work, OneBlood, Quest Diagnostics, Tampa General Hospital, Takeda, among others. Browse the listings above to see every active employer.

Are there remote or hybrid Science & Research jobs in Tampa, FL?

Yes — 7 of the 17 open science & research positions offer remote or hybrid work (4 remote, 3 hybrid).

How do I apply for Science & Research jobs in Tampa, FL?

Each listing links directly to the employer's application page. Apply early — fresh listings get the most recruiter attention in the first two weeks.

Science & Research Jobs in Tampa, FL

DEPARTMENT: Data Insights and Innovation

JOB TITLE: AI Governance & Explainability Engineer

JOB CODE: AIGEE

REPORTS TO: Data Governance Lead

FLSA STATUS: Exempt

EMPLOYMENT TYPE: Full-Time

JOB PURPOSE:

This role at Arbitration Forums is as unique as it is rewarding because of the AF IPAAL Values (Integrity, Passion, Accountability, Achievement, Leadership) and TRI Model (Trust, Respect, Inclusion).

The AI Governance & Explainability Engineer is a hands‑on technical role within the Data Governance team responsible for ensuring AI, GenAI, and Agentic AI solutions are explainable, governable, auditable, and production‑ready.

This role embeds governance directly into the AI technology stack, translating policies, regulatory expectations, and risk requirements into technical controls, automated checks, standardized artifacts, and release gates across the AI lifecycle.

The role combines AI/ML engineering depth, GenAI & Agentic AI design knowledge, and governance discipline to ensure AI solutions deliver explainability, can be trusted, defended, and audited in production, particularly within the Microsoft Fabric and Purview ecosystem.

DEPARTMENTAL EXPECTATION OF EMPLOYEE

Adheres to AF Policy and Procedures and the AF IPAAL Values and TRI Model

Acts as a role model within and outside AF.
Performs duties as workload necessitates.
Maintains a positive and respectful attitude.
Communicates regularly with the departmental leader about department issues.
Demonstrates flexible and efficient time management and ability to prioritize workload.
Consistently reports to work on time, prepared to perform duties of the position.
Meets Department productivity standards.

ESSENTIAL DUTIES AND RESPONSIBILITIES

AI Governance by Design Engineering (Execution Focus not Policy writing)
- Embed governance, explainability, and risk controls directly into AI, GenAI, and Agentic AI workflows
- Translate enterprise AI policies, standards, and Responsible AI principles into:
  - Technical guardrails
  - Automated checks
  - Required evidence artifacts
  - CI/CD release gates
- Implement governance as code and automation, eliminating reliance on manual or after-the-fact reviews.
AI Governance, Explainability & Human Oversight
- Advise solution teams on explainability requirements for automated, semi-automated, and decision-support AI systems.
- Ensure human-in-the-loop (HITL) controls are implemented where required by risk level or use case.
- Define, generate, and manage explainability outputs that are:
  - Appropriate to the end-user or reviewer persona
  - Aligned to the decision context and operational use
- Document explainability assumptions, limitations, and residual risk as governance evidence.
Metadata, Lineage & Governance Evidence Management
- Operationalize AI Governance in Microsoft Purview by registering and maintaining:
  - AI models, features, prompts, agents, notebooks, and pipelines
- Maintain end to end lineage across:
  - Data → features → models → inferences → outputs
  - Apply ownership, stewardship, sensitivity, and classification metadata.
- Ensure governance is maintained:
  - Discoverable
  - Versioned
  - Traceable
  - Audit-defensible

GenAI & Agentic AI Governance Enablement
- Apply governance patterns to LLMs, RAG, and Agentic AI solutions
- Ensure governance traceability when synthetic data or augmented data is used for training, testing, or evaluation.

Implement Agentic AI lifecycle governance, including:
- Observability of agent actions, deviations, and failures
- Oversight of planning, reflection, and tool-use behavior
- Controls on autonomous vs. constrained operation
Enable GenAI explainability, including:
- Retrieval transparency for RAG (sources, relevance)
- Inference context documentation
- Decision trace generation where applicable

Explainability, Interpretability & Model Risk Controls
- Own and operate explainability capabilities used for governance, audit, and trust.
- Implement and operationalize techniques such as:
  - Feature attribution (e.g., SHAP or equivalent)
  - Driver and proxy detection
  - Global and local model explanations
- Identify bias signals, risk indicators, and explainability gaps.
- Store and manage explainability and observability outputs as governed, audit-ready artifacts.
- Support audit, compliance, and risk review activities with defensible evidence.

Monitoring, Observability & Incident Readiness
- Define and implement AI monitoring metrics, alerts, and thresholds for:
  - Performance degradation
  - Bias and ethical risk indicators
  - Drift and instability
- Partner with MLOps and platform teams to integrate monitoring into production pipelines.
- Support AI incident response and post-incident reviews with governance evidence.
- Ensure all observability outputs are retained, traceable, and audit‑ready.
Governance Checkpoints & Release Gating
- Define and enforce governance checkpoints within CI/CD pipelines (DEV-> TEST/UAT -> PROD).
- Implement automated release checks for:
  - Required documentation and evidence artifacts
  - Explainability artifacts
  - Monitoring configuration
  - Data usage, lineage completeness, and medallion-layer alignment
- Partner with Engineering and MLOps teams on promotion decisions while owning governance readiness, not platform approval.

QUALIFICATIONS

Required Qualifications

Bachelor’s or Master’s degree in Computer Science, Information Systems, Data Science, Engineering, or a related field.
Minimum 7 years of experience in AI/ML engineering, data science, GenAI/LLMs, NLP, Agentic AI, data governance, or related roles.
Demonstrated experience operationalizing AI governance, explainability, and risk controls in production environments.
Deep understanding of Agentic AI architectures and lifecycle considerations.

Technical Skills

Strong proficiency in Python with hands‑on experience in AI/ML engineering workflows.
Working knowledge of Microsoft Fabric (Lakehouse, OneLake, notebooks, pipelines).
Experience with Microsoft Purview (catalog, lineage, classification, ownership).

Experience with AI/ML and GenAI tooling, including:

Azure AI Foundry / Azure ML
ML explainability libraries (e.g., SHAP)
LLMs, RAG architecture, and prompt engineering
Familiarity with Agentic AI frameworks and patterns (e.g., tool use, planning, reflection).
Experience integrating governance controls into CI/CD pipelines using GitHub or Azure DevOps.
Understanding of cloud platforms (Azure preferred; AWS/GCP a plus
Experience producing audit‑ready technical documentation and evidence artifacts.
Familiarity with reporting and visualization tools (e.g., Power BI) for governance and monitoring views.

Soft Skills

Strong analytical and problem‑solving abilities, particularly in risk‑based decision‑making.
Excellent written and verbal communication skills, with the ability to translate technical details into governance‑relevant insights.
Ability to lead governance execution initiatives and influence cross‑functional teams without direct authority.
Strong organizational skills with attention to detail and audit readiness.
Auto insurance or claims industry experience preferred.

Preferred Qualifications

Experience evaluating or governing model training approaches (e.g., NLP, generative models) without owning full training pipelines.
Familiarity with synthetic data governance (generation methods, limitations, risk documentation).
Experience with additional AI platforms (Databricks AI, Snowflake Cortex, Dataiku).
Experience in regulated industries (insurance, financial services, healthcare).

AMERICANS WITH DISABILITY SPECIFICATIONS

PHYSICAL DEMANDS

The physical demands described here are representative of those that must be met by an employee to successfully perform the essential functions of this job.

While performing the duties of this job, the employee is occasionally required to stand; walk; sit; use hands to finger, handle, or feel objects, tools, or controls; reach with hands and arms; climb stairs; balance; stoop, kneel, crouch, or crawl; talk or hear; taste or smell. The employee must occasionally lift and/or move up to 25 pounds. Specific vision abilities required by the job include close vision, distance vision, color vision, peripheral vision, depth perception, and the ability to adjust focus.

WORK ENVIRONMENT [Standard language tied to each job description]

This is a fully remote position requiring reliable high-speed internet access and a dedicated workspace.

Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.

DEPARTMENT: Data Insights and Innovation

JOB TITLE: AI Governance & Explainability Engineer

JOB CODE: AIGEE

REPORTS TO: Data Governance Lead

FLSA STATUS: Exempt

EMPLOYMENT TYPE: Full-Time

JOB PURPOSE:

This role at Arbitration Forums is as unique as it is rewarding because of the AF IPAAL Values (Integrity, Passion, Accountability, Achievement, Leadership) and TRI Model (Trust, Respect, Inclusion).

DEPARTMENTAL EXPECTATION OF EMPLOYEE

Adheres to AF Policy and Procedures and the AF IPAAL Values and TRI Model

Acts as a role model within and outside AF.
Performs duties as workload necessitates.
Maintains a positive and respectful attitude.
Communicates regularly with the departmental leader about department issues.
Demonstrates flexible and efficient time management and ability to prioritize workload.
Consistently reports to work on time, prepared to perform duties of the position.
Meets Department productivity standards.

ESSENTIAL DUTIES AND RESPONSIBILITIES

AI Governance by Design Engineering (Execution Focus not Policy writing)
- Embed governance, explainability, and risk controls directly into AI, GenAI, and Agentic AI workflows
- Translate enterprise AI policies, standards, and Responsible AI principles into:
  - Technical guardrails
  - Automated checks
  - Required evidence artifacts
  - CI/CD release gates
- Implement governance as code and automation, eliminating reliance on manual or after-the-fact reviews.
AI Governance, Explainability & Human Oversight
- Advise solution teams on explainability requirements for automated, semi-automated, and decision-support AI systems.
- Ensure human-in-the-loop (HITL) controls are implemented where required by risk level or use case.
- Define, generate, and manage explainability outputs that are:
  - Appropriate to the end-user or reviewer persona
  - Aligned to the decision context and operational use
- Document explainability assumptions, limitations, and residual risk as governance evidence.
Metadata, Lineage & Governance Evidence Management
- Operationalize AI Governance in Microsoft Purview by registering and maintaining:
  - AI models, features, prompts, agents, notebooks, and pipelines
- Maintain end to end lineage across:
  - Data → features → models → inferences → outputs
  - Apply ownership, stewardship, sensitivity, and classification metadata.
- Ensure governance is maintained:
  - Discoverable
  - Versioned
  - Traceable
  - Audit-defensible

GenAI & Agentic AI Governance Enablement
- Apply governance patterns to LLMs, RAG, and Agentic AI solutions
- Ensure governance traceability when synthetic data or augmented data is used for training, testing, or evaluation.

Implement Agentic AI lifecycle governance, including:
- Observability of agent actions, deviations, and failures
- Oversight of planning, reflection, and tool-use behavior
- Controls on autonomous vs. constrained operation
Enable GenAI explainability, including:
- Retrieval transparency for RAG (sources, relevance)
- Inference context documentation
- Decision trace generation where applicable

Explainability, Interpretability & Model Risk Controls
- Own and operate explainability capabilities used for governance, audit, and trust.
- Implement and operationalize techniques such as:
  - Feature attribution (e.g., SHAP or equivalent)
  - Driver and proxy detection
  - Global and local model explanations
- Identify bias signals, risk indicators, and explainability gaps.
- Store and manage explainability and observability outputs as governed, audit-ready artifacts.
- Support audit, compliance, and risk review activities with defensible evidence.

Monitoring, Observability & Incident Readiness
- Define and implement AI monitoring metrics, alerts, and thresholds for:
  - Performance degradation
  - Bias and ethical risk indicators
  - Drift and instability
- Partner with MLOps and platform teams to integrate monitoring into production pipelines.
- Support AI incident response and post-incident reviews with governance evidence.
- Ensure all observability outputs are retained, traceable, and audit‑ready.
Governance Checkpoints & Release Gating
- Define and enforce governance checkpoints within CI/CD pipelines (DEV-> TEST/UAT -> PROD).
- Implement automated release checks for:
  - Required documentation and evidence artifacts
  - Explainability artifacts
  - Monitoring configuration
  - Data usage, lineage completeness, and medallion-layer alignment
- Partner with Engineering and MLOps teams on promotion decisions while owning governance readiness, not platform approval.

QUALIFICATIONS

Required Qualifications

Bachelor’s or Master’s degree in Computer Science, Information Systems, Data Science, Engineering, or a related field.
Minimum 7 years of experience in AI/ML engineering, data science, GenAI/LLMs, NLP, Agentic AI, data governance, or related roles.
Demonstrated experience operationalizing AI governance, explainability, and risk controls in production environments.
Deep understanding of Agentic AI architectures and lifecycle considerations.

Technical Skills

Strong proficiency in Python with hands‑on experience in AI/ML engineering workflows.
Working knowledge of Microsoft Fabric (Lakehouse, OneLake, notebooks, pipelines).
Experience with Microsoft Purview (catalog, lineage, classification, ownership).

Experience with AI/ML and GenAI tooling, including:

Azure AI Foundry / Azure ML
ML explainability libraries (e.g., SHAP)
LLMs, RAG architecture, and prompt engineering
Familiarity with Agentic AI frameworks and patterns (e.g., tool use, planning, reflection).
Experience integrating governance controls into CI/CD pipelines using GitHub or Azure DevOps.
Understanding of cloud platforms (Azure preferred; AWS/GCP a plus
Experience producing audit‑ready technical documentation and evidence artifacts.
Familiarity with reporting and visualization tools (e.g., Power BI) for governance and monitoring views.

Soft Skills

Strong analytical and problem‑solving abilities, particularly in risk‑based decision‑making.
Excellent written and verbal communication skills, with the ability to translate technical details into governance‑relevant insights.
Ability to lead governance execution initiatives and influence cross‑functional teams without direct authority.
Strong organizational skills with attention to detail and audit readiness.
Auto insurance or claims industry experience preferred.

Preferred Qualifications

Experience evaluating or governing model training approaches (e.g., NLP, generative models) without owning full training pipelines.
Familiarity with synthetic data governance (generation methods, limitations, risk documentation).
Experience with additional AI platforms (Databricks AI, Snowflake Cortex, Dataiku).
Experience in regulated industries (insurance, financial services, healthcare).

AMERICANS WITH DISABILITY SPECIFICATIONS

PHYSICAL DEMANDS

The physical demands described here are representative of those that must be met by an employee to successfully perform the essential functions of this job.

WORK ENVIRONMENT [Standard language tied to each job description]

This is a fully remote position requiring reliable high-speed internet access and a dedicated workspace.

Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.

Science & Research Jobs in Tampa, FL (Now Hiring) — 17 open

AI Governance & Explainability Engineer

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Frequently asked questions

How many Science & Research jobs are open in Tampa, FL right now?

Which companies are hiring for Science & Research roles in Tampa, FL?

Are there remote or hybrid Science & Research jobs in Tampa, FL?

How do I apply for Science & Research jobs in Tampa, FL?

Science & Research jobs in other cities

Other jobs in Tampa, FL

AI Governance & Explainability Engineer

Job description

Tools

Explore

Company

Tools

Explore

Company

Science & Research Jobs in Tampa, FL (Now Hiring) — 17 open

AI Governance & Explainability Engineer

Lab Assistant (Specimen Technician I)

Lab Assistant (Specimen Technician I)

Lab Assistant (Specimen Technician I)

Lab Assistant (Specimen Technician I)

School of Medical Laboratory Science - Lab Assistant

School of Medical Laboratory Science - Lab Assistant

Medical Technologist

Patient Access & Reimbursement Case Manager Operations Leadership - Tampa, FL

Phlebotomy Supervisor

Medical Technologist

Medical Screener

Part-time Medical Retina Specialist

Business Account Executive, TTR - Tampa, FL

DIRECTOR OF ENVIRONMENTAL

Postdoctoral Fellow of Research

Cardiovascular Invasive Specialist 2

Teaching Laboratory Specialist

Associate/Full Professor-Associate Dean, Faculty Development

Cardiovascular Invasive Specialist 2

Frequently asked questions

How many Science & Research jobs are open in Tampa, FL right now?

Which companies are hiring for Science & Research roles in Tampa, FL?

Are there remote or hybrid Science & Research jobs in Tampa, FL?

How do I apply for Science & Research jobs in Tampa, FL?

Science & Research jobs in other cities

Other jobs in Tampa, FL

AI Governance & Explainability Engineer

Job description

Tools

Explore

Company