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Explainable Ai Xai Jobs (NOW HIRING)

Consip S.P.A.

NY · On-site

$80 - $120/hr

Applicare metodologie di Explainable AI (XAI) per migliorare la comprensibilità dei modelli * Utilizzare pipeline dati interoperabili e conformi a UNI CEI EN ISO/IEC 8183 e UNI CEI ISO/IEC 42001, in ...

InstantServe LLC

Data Scientist

Apply explainable AI (XAI) techniques and Responsible AI frameworks (NIST AI RMF) * Deliver secure solutions in AWS GovCloud or Azure FedRAMP environments * Support ATO processes and ensure ...

ClifyX

GenAI Engineer

Wilmington, DE · On-site

... explainable AI (XAI) for transparent decision-making • Optimize cost, latency, and scalability of AI systems • Troubleshoot AI/ML system issues across data and deployment layers • Write ...

3B Staffing LLC

Integration Architect

East Hartford, CT · On-site

$70.25 - $90.50/hr

Preferred experience with explainable AI (XAI) techniques. Preferred experience with MLOps and model deployment pipelines. Preferred experience with containerization technologies (e.g., Docker ...

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3B Staffing LLC

Integration Architect

East Hartford, CT · On-site

$70.25 - $90.50/hr

Preferred experience with explainable AI (XAI) techniques. Preferred experience with MLOps and model deployment pipelines. Preferred experience with containerization technologies (e.g., Docker ...

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Explainable Ai Xai information

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$46.5K

$94.5K

$142K

How much do explainable ai xai jobs pay per year?

As of Aug 13, 2026, the average yearly pay for explainable ai xai in the United States is $94,542.00, according to ZipRecruiter salary data. Most workers in this role earn between $73,000.00 and $95,500.00 per year, depending on experience, location, and employer.

What are some common challenges faced by professionals working in explainable AI (XAI) roles?

Professionals in Explainable AI often encounter the challenge of balancing model accuracy with interpretability, as more complex models can be harder to explain. Another common difficulty is effectively communicating technical findings to non-technical stakeholders, ensuring that explanations are both accurate and accessible. Additionally, XAI specialists must stay updated with rapidly evolving regulations and best practices, as transparency requirements continue to grow in industries like finance and healthcare. Collaboration with data scientists, product managers, and compliance teams is also a regular part of the role, requiring strong interdisciplinary communication skills.

What is explainable AI (XAI)?

Explainable AI (XAI) refers to methods and techniques in artificial intelligence that make the decisions and outputs of AI systems understandable and interpretable to humans. Unlike traditional 'black box' AI models, XAI aims to provide clear explanations about how and why a particular decision was made by the system. This transparency is crucial in fields like healthcare, finance, and legal, where understanding the reasoning behind AI decisions is essential for trust, accountability, and compliance. XAI can use various approaches, such as visualizations, simplified models, or feature importance scores, to explain predictions in a user-friendly way.

What are the key skills and qualifications needed to thrive as an explainable AI (XAI) specialist, and why are they important?

To thrive as an Explainable AI (XAI) Specialist, you need a strong background in machine learning, statistics, and computer science, typically supported by an advanced degree in a related field. Familiarity with technical tools such as Python, TensorFlow, and specialized XAI libraries like LIME or SHAP, as well as knowledge of regulatory standards, is essential. Strong communication skills, critical thinking, and the ability to translate complex technical concepts for non-technical stakeholders are crucial soft skills. These abilities ensure that AI systems are transparent, trustworthy, and ethically aligned with business and regulatory requirements.

What is the difference between Explainable Ai Xai vs Data Scientist?

AspectExplainable Ai XaiData Scientist
Required CredentialsTypically requires knowledge of AI, machine learning, and data analysis; certifications in AI or data science are commonRequires degrees in computer science, statistics, or related fields; certifications in data analysis or machine learning are beneficial
Work EnvironmentOften in AI development teams, focusing on model transparency and interpretabilityIn research, analytics, or product teams, focusing on data modeling, analysis, and insights
Industry UsageUsed across tech, finance, healthcare for AI transparency and complianceUsed across industries for data analysis, predictive modeling, and decision support

Explainable Ai Xai focuses on making AI models transparent and understandable, often working closely with AI development teams. Data Scientists analyze data, build models, and generate insights. While both roles involve data and machine learning, Xai emphasizes model interpretability, whereas Data Scientists focus on data analysis and predictive modeling.

Infographic showing various Explainable Ai Xai job openings in the United States as of August 2026, with employment types broken down into 33% Full Time, and 67% Contract. Highlights an 100% In-person job distribution, with an average salary of $94,542 per year, or $45.5 per hour.

AI Data Scientist

Consip S.P.A.

NY • On-site

$80 - $120/hr

Other

Posted 8 days ago

Job description

Cerchiamo Persone che portino competenza nelle sfide di ogni giorno, mettendo passione in ciò che fanno e scegliendo la trasparenza come stile di lavoro e di relazione, nei processi e nel dialogo con gli stakeholder. Persone che guardino al futuro con curiosità, concretezza e spirito di innovazione, per contribuire alla crescita dell’Azienda e del Sistema Paese. L’AI Data Scientist, in particolare, adotta tecniche avanzate di analisi statistica e machine learning, assicurando qualità, preparazione e affidabilità dei dataset. Il ruolo opera a supporto dei processi decisionali e dell’innovazione digitale, combinando competenze statistiche, programmazione e conoscenza del dominio per sviluppare modelli predittivi e soluzioni avanzate basate sui dati, in contesti organizzativi complessi e regolati.

Principali attività/responsabilità
  • Analizzare dati complessi per identificare insight strategici e opportunità di business
  • Sviluppare modelli predittivi e di machine learning per risolvere problemi aziendali
  • Creare visualizzazioni e report per comunicare i risultati delle analisi
  • Collaborare con team aziendali per integrare i dati nei processi decisionali
  • Garantire la qualità e la tracciabilità dei dati e la conformità legislativa attraverso best practice, incluso il rispetto della legislazione vigente
  • Applicare metodologie di Explainable AI (XAI) per migliorare la comprensibilità dei modelli
  • Utilizzare pipeline dati interoperabili e conformi a UNI CEI EN ISO/IEC 8183 e UNI CEI ISO/IEC 42001, in collaborazione con l’AI Data Engineer
  • Ottimizzare l'efficienza computazionale e la sostenibilità energetica dei modelli AI
  • Laurea Magistrale in Informatica, Ingegneria, Matematica, Fisica o altre discipline STEM
  • Esperienza lavorativa pregressa di almeno 3 anni maturata, anche in ambito accademico, in almeno uno dei seguenti ambiti:
    • A) utilizzo di tecniche avanzate di analisi statistica e machine learning per l’estrazione di pattern, insight e previsioni su dati strutturati e/o non strutturati
    • B) sviluppo, validazione e testing di modelli di machine learning a supporto di esigenze operative o di business
    • C) utilizzo di linguaggi di programmazione per data science e delle relative librerie per l’implementazione di analisi e modelli
    • D) produzione di report e analisi interpretative per la comunicazione dei risultati a stakeholder tecnici e non tecnici
Requisiti preferenziali
  • I. Conoscenza dei principi di gestione e governance dei dati (metodologie di validazione dei dati), inclusi qualità, accessibilità e sicurezza dei dataset
  • II. Conoscenza delle metriche e norme per misurare qualità, accuratezza e coerenza dei dati
  • III. Conoscenza delle metodologie di Explainable AI (XAI) e di valutazione dei bias
  • IV. Buona conoscenza della lingua inglese in ambito professionale
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