# StarCompliance — Head of Data and AI - EAST COAST

- Generated: 2026-09-21 08:02:50 AM EDT
- Original/canonical posting: https://www.linkedin.com/jobs/view/4469868371/
- Provider: LinkedIn
- Posted: approximately 2026-09-21 07:02 AM EDT (from '1 hour ago')
- Elapsed since posting: represented by the provider's relative posting label and approximate Eastern timestamp when available
- Applicants: Be among the first 25 applicants
- Work model/location: East Coast requirement; LinkedIn location Miami-Fort Lauderdale; exact remote arrangement is unverified
- Compensation: Not disclosed; Head title qualifies for missing-compensation review
- Travel: Not disclosed
- Positioning track: Executive leader
- Fit outcome: PASS — 88%

## Direct-match strengths

Hands-on Data/AI strategy, production GenAI and ML, LLM architectures, RAG, agents, vector search, data engineering, pipelines, Snowflake, cloud-native services, MLOps, monitoring, drift, regulated financial-services technology, team building, AI-enabled SaaS, roadmap ownership, and executive collaboration.

## Hard or material gaps

PASS uses the 80%+ verification exception because the exact remote arrangement is not explicit. Keith's cloud depth is AWS-led rather than Azure-led, and his strongest production evidence is applied LLM/RAG/agent platforms rather than foundation-model fine-tuning, recommendation systems, or Azure Data Factory/Synapse/OpenAI/ML. Those are material but not central enough to outweigh the direct mandate match.

## Evidence map

1. Own Data and AI strategy and execution (weight 3, evidence 3/3) — Direct strategy, architecture, product, engineering, and operations ownership.
2. Production GenAI, RAG, agents, vector and semantic search (weight 3, evidence 3/3) — Direct production evidence across the stack.
3. Predictive analytics, graph intelligence, recommendations (weight 2, evidence 2/3) — ML and graph work are direct; recommendation depth is less explicit.
4. Modern data engineering, pipelines, and Snowflake (weight 3, evidence 3/3) — Direct data platforms, ETL/ELT, pipelines, warehousing, and Snowflake.
5. MLOps, monitoring, experiments, and drift (weight 3, evidence 3/3) — Direct evaluation, monitoring, versioning, releases, bias/drift, and operations.
6. Azure data and AI ecosystem (weight 3, evidence 1/3) — Equivalent hyperscaler is allowed, but Keith's deepest experience is AWS.
7. Cloud-native microservices and event-driven architecture (weight 2, evidence 3/3) — Direct implementation evidence.
8. Build and lead Data/AI teams (weight 3, evidence 3/3) — Built and led multidisciplinary AI/data teams.
9. Financial services, compliance, and regulated domains (weight 2, evidence 3/3) — Direct regulated financial-services privacy and AI governance.
10. Verified eligible work model (weight 2, evidence 1/3) — East Coast is explicit; exact remote arrangement is not.

## Keyword diagnostic

Strong hands-on Data/AI leadership, regulated SaaS, data-platform, production GenAI/agents, governance, team, and roadmap match; Azure and model-training depth are caveats.

## Full normalized job description

About StarCompliance
Are you based on the East Coast and a hands on technical Data Science Leader?
StarCompliance is a market-leading provider of employee compliance technology, trusted by 350+ global financial institutions and highly regulated organisations to manage regulatory obligations, monitor employee activity, and mitigate risk. Backed by private equity, we are investing in the next generation of our cloud-native, AI-enabled SaaS platform — and this role will shape the AI and data future of that platform.
Role:
We need a hands-on technical leader to own our data and AI strategy and execute it — building a high-performing Data & AI function that ships production-grade AI capabilities at scale in a heavily regulated environment.
You will partner closely with Product, Engineering, Architecture, Security, and the executive team. You will work together with the Product Director, AI & Data Products as a joint technology-and-product leadership pairing, co-owning the AI roadmap, prioritisation, and delivery outcomes.
Responsibilities:
AI Product Delivery & Strategy
Own the end-to-end technical strategy and execution roadmap for AI-enabled product capabilities across the StarCompliance platform.
Drive adoption of generative AI, LLM-based architectures, predictive analytics, graph intelligence, recommendation systems, and semantic search where these deliver measurable customer value.
Data Platform & Engineering
Own the data foundation strategy, ensuring clean, trusted, well-governed data underpins every AI initiative.
Build robust MLOps capabilities, including model training pipelines, versioning, monitoring, A/B experimentation, and drift detection.
Internal AI Enablement
Champion pragmatic AI adoption within engineering and product development, accelerating how we build, not just what we build.
Leadership & Team Building
Build, mentor, and scale a high-performing Data & AI organisation across data engineering, data science, ML engineering and analytics.
Cross-Functional & Executive Collaboration
Work closely with the CTO and executive team to align AI and data initiatives with company strategy and commercial priorities.
Partner with the Product Director, AI & Data Products, co-owning the AI roadmap, prioritisation, and delivery outcomes.
Skills and Experience:
AI & Machine Learning
Proven delivery of production-grade AI and ML systems at scale, not just experimentation.
Deep experience with generative AI and LLM-based application architectures (fine-tuning, prompt engineering, RAG, agentic frameworks).
Strong knowledge of vector databases and semantic search (e.g. Pinecone, Weaviate, pgvector, Azure AI Search).
Data Engineering & Platforms
Deep background in modern data engineering, ELT/ETL patterns, and large-scale data pipeline architectures.
Hands-on experience with Snowflake (or equivalent cloud data warehouse) and associated data modelling patterns.
Strong Azure ecosystem experience: Azure Data Factory, Azure Synapse, Azure OpenAI Service, and Azure Machine Learning.
Software Engineering & Architecture
Strong software engineering fundamentals and the credibility to engage at technical depth with senior engineers.
Experience with cloud-native, microservices, and event-driven architectures on Azure (or equivalent hyperscaler).
Ability to make sound build vs buy vs integrate decisions across the AI and data tooling landscape.
Leadership & Business
Proven experience building and leading high-performing Data / AI engineering teams.
Experience within financial services, regtech, compliance, surveillance, or similarly regulated domains.
Strong commercial instincts — the ability to connect technical investment to customer value and business outcomes.
Integrity and Ethics
All StarCompliance employees are expected to commit to a high standard of personal integrity and carry out their responsibilities in an ethical manner.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.

## Artifact metadata

- Resume: https://bit.ly/47baLHI
- Cover letter: https://bit.ly/479NFRO
- Validation: PASS — 2-page resume (949 words), 1-page cover letter (217 words); PDF geometry, bounds, annotations, links, and visual pages verified.
- Google Drive used: No