Lead AI & Data Scientist

Trustwell

1d•$150,000 - $170,000•Remote

About The 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.

Requirements

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 review, auditability, and acceptable use controls
Strong analytical skills with the ability to work with structured and unstructured data
Experience with Python and common data science, machine learning, or AI development libraries and frameworks
Familiarity with APIs, cloud services, data pipelines, vector stores, and production software delivery practices
Experience defining quality measurement approaches for AI systems, including benchmark datasets, test cases, quality scoring, drift monitoring, and regression evaluation
Strong communication skills with the ability to explain AI concepts, risks, trade-offs, and implementation plans to technical and non-technical stakeholders
Pragmatic judgment with the ability to balance innovation, governance, speed, cost, and risk
Collaborative, approachable working style with the ability to influence across teams without direct authority

Nice To Haves

Advanced degree in a quantitative, technical, or AI-related discipline preferred but not required
Experience implementing generative AI or LLM-based capabilities in a SaaS, software, regulatory, compliance, supply chain, or data-intensive business environment
Experience with AI governance frameworks, model risk management, responsible AI programs, or enterprise AI operating models
Experience with OpenAI, Anthropic, Google, AWS, Azure, or similar AI/ML platforms
Experience with vector databases or retrieval systems such as Chroma, Pinecone, Weaviate, OpenSearch, MongoDB Atlas Vector Search, or similar technologies
Experience with data platforms such as MongoDB, Snowflake, SQL Server, PostgreSQL, or cloud-native data services
Experience with cloud environments, preferably AWS
Experience with MLOps, LLMOps, observability, evaluation pipelines, model monitoring, and AI cost management
Experience working with product and engineering teams to move AI prototypes into production
Experience creating internal AI usage policies, review boards, enablement materials, training programs, or implementation playbooks
Experience with compliance-sensitive environments involving customer data, privacy requirements, auditability, or regulated industries

Responsibilities

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.