Lead AI & Data Scientist

Role: Lead AI & Data Scientist

FLSA: Full Time | Exempt | Salaried | Remote (US)

Reports to: Chief Technology Officer

Scope of Position: We are seeking a Lead AI & Data Scientist to help define, implement, and operationalize Trustwell’s artificial intelligence strategy across products, engineering, data, and internal operations. This role is responsible for turning our existing and new AI systems into reliable, governed, measurable, and reusable capabilities that create practical business value.

The ideal candidate has direct experience implementing AI within an organization, including production use cases, governance models, adoption processes, data controls, evaluation frameworks, and cross-functional enablement. This person should be equally comfortable working with business leaders on AI strategy, engineers on implementation patterns, product teams on customer-facing use cases, and security/compliance stakeholders on responsible AI controls.

This is a high-impact role for someone who understands applied AI, data science, machine learning, LLMs, agentic workflows, data governance, and enterprise adoption. The role requires strong technical judgment, practical implementation experience, and the ability to create structure in a rapidly evolving technology area.

Essential Duties &

Responsibilities including but limited to: Define and help execute Trustwell’s applied AI strategy across product, engineering, data, and internal business operations Lead the implementation of AI capabilities that are practical, measurable, secure, and aligned to business outcomes Establish AI governance practices, including usage policies, risk controls, evaluation standards, approval processes, and ongoing monitoring Partner with Engineering to design repeatable AI implementation patterns for LLMs, agents, retrieval-augmented generation, structured outputs, model evaluation, observability, and production operations Partner with Product to identify, evaluate, and prioritize AI-enabled product opportunities that improve customer value and operational efficiency Develop frameworks for assessing AI use cases, including feasibility, risk, data availability, implementation complexity, cost, and expected business impact Create and maintain standards for responsible AI usage, including data handling, prompt management, model selection, explainability, auditability, and human-in-the-loop controls Build and guide AI evaluation processes, including test datasets, regression testing, hallucination detection, quality scoring, accuracy measurement, and production feedback loops Help establish internal AI adoption processes, including development workflows, engineering enablement, training, approved tooling, and practical usage guidelines Work with Security, Legal, Compliance, and IT stakeholders to ensure AI implementations align with Trustwell’s data protection, privacy, contractual, and security obligations Analyze structured and unstructured data to identify opportunities for automation, prediction, classification, summarization, enrichment, and decision support Develop prototypes, proofs of concept, and production-ready analytical or AI workflows where appropriate Provide technical leadership on model selection, vendor evaluation, build-versus-buy decisions, AI cost management, and long-term platform strategy Collaborate with data engineering and application teams to improve data readiness, data quality, metadata, lineage, and retrieval strategies for AI-enabled systems Define metrics and reporting to measure AI adoption, performance, quality, cost, risk, and business impact Act as a trusted advisor and mentor to engineering, product, and business teams as they adopt AI responsibly and effectively Other duties as required.

Required

Skills/Abilities 8+ years of professional experience across data science, machine learning, AI engineering, data engineering, software engineering, or related technical disciplines, with demonstrated experience leading applied AI initiatives from concept through implementation

Direct experience establishing or operating AI governance, evaluation, adoption, or production-readiness processes within an organization

Hands-on experience implementing AI or machine learning capabilities in a production, enterprise, or customer-facing environment

Strong understanding of modern AI technologies, including LLMs, embeddings, retrieval-augmented generation, prompt engineering, structured outputs, agentic workflows, and model evaluation

Experience translating business problems into practical AI, data science, analytics, or automation solutions

Ability to evaluate AI use cases based on business value, technical feasibility, data readiness, operational risk, and implementation cost

Experience establishing responsible AI practices, including data privacy, security, bias awareness, explainability, human revi