# Nesco Resource — Director of Applied AI & Data Science

- Generated: 2026-09-10 09:40:03 PM EDT
- Original/canonical posting: https://www.linkedin.com/jobs/view/4465859417/
- Provider: LinkedIn
- Posted: approximately 2026-09-10 04:40 PM EDT (from '5 hours ago')
- Elapsed since posting: represented by the provider's relative posting label and approximate Eastern timestamp
- Applicants: Be among the first 25 applicants
- Work model/location: United States; remote status is not verified. The exact underlying employer role was hybrid in New Haven, Connecticut
- Compensation: $140,000-$238,000 base on the underlying Knights of Columbus posting
- Travel: Not disclosed
- Positioning track: Technical manager
- Fit outcome: FAIL — 86%

## Direct-match strengths

Production GenAI/RAG, Python, SQL, PostgreSQL, Snowflake exposure, APIs, pipelines, integration, monitoring, CI/CD, responsible AI, model transparency, drift, technical leadership, and insurance/financial-services governance.

## Hard or material gaps

The listing duplicates the Knights of Columbus Director role, whose employer posting now says the position has been filled. The recruiter repost provides no authoritative remote designation; the exact employer role was hybrid in New Haven, Connecticut, which conflicts with Keith's location constraints. Actuarial and claims-modeling depth is also not established.

## Evidence map

1. Production AI/ML and GenAI systems (weight 3, evidence 3/3) — Direct production platform and model-lifecycle experience.
2. Python, SQL, and production engineering (weight 3, evidence 3/3) — Direct and longstanding hands-on evidence.
3. Snowflake, PostgreSQL, and enterprise data (weight 3, evidence 2/3) — Direct PostgreSQL and enterprise data depth; Snowflake exposure is supported but less central.
4. APIs, pipelines, and integration (weight 2, evidence 3/3) — Direct production API, data-pipeline, and system-integration work.
5. Monitoring, CI/CD, lifecycle, and drift (weight 3, evidence 3/3) — Direct production operations, evaluation, observability, CI/CD, and drift-governance evidence.
6. Lead technical teams (weight 2, evidence 3/3) — Led multiple engineering, AI, and data teams, including managers.
7. Insurance, responsible AI, and regulation (weight 2, evidence 3/3) — Direct MassMutual insurance, privacy, Responsible AI, and governance evidence.
8. Actuarial and claims modeling (weight 3, evidence 1/3) — Transferable predictive-analytics experience, but actuarial/claims depth is not established.
9. Open and eligible work model (weight 3, evidence 0/3) — Underlying role is filled; the employer posting was hybrid in Connecticut.

## Keyword diagnostic

Strong production-AI and regulated-insurance alignment, independently disqualified by the closed underlying role and work-model/location conflict.

## Full normalized job description

Director of Applied AI & Data Science
Overview
The Director of Applied AI & Data Science is a senior technical leader responsible for designing, engineering and operationalizing production grade AI solutions across Life Insurance, Investments, Membership, and Charities. This role focuses on building scalable and reliable AI systems, including machine learning and generative AI (LLM) capabilities, within a governed enterprise data and AI ecosystem.
This role provides technical leadership and oversight for the Senior Applied AI & Data Scientist, Applied AI & Data Scientist, and Senior Data & AI Solutions Architect. The Director of Applied AI & Data Science ensures solutions are production-ready, resilient, cost-effective, and aligned to responsible AI and regulatory standards.
Core Responsibilities
Develop and deploy advanced statistical, machine learning, and AI models supporting insurance, actuarial, claims, and investment decisionmaking
Design and deliver LLMenabled analytics and Deep Research solutions, including RAGbased approaches over structured and unstructured data
Perform handson data analysis, feature engineering, and model development using enterprise platforms (Snowflake, PostgreSQL, DB2)
Define model success metrics, validation approaches, and explainability standards aligned to business and regulatory expectations
Partner with data engineers and architects to produce models, establish monitoring, and manage model lifecycle and drift
Apply responsible AI practices including bias assessment, transparency, and documentation for audit and compliance review
Communicate analytical insights and model outcomes clearly to business leaders and technical stakeholders
Collaborate across analytics, engineering, risk, and compliance teams to ensure scalable, governed AI and analytics deliver
Skills Qualifications
Required :
Strong experience with Python and SQL and production system development.
Experience with Snowflake and relational databases such as PostgreSQL.
Experience with API development, pipelines, and system integration.
Experience with monitoring, CI/CD, and operational lifecycle management of AI systems.
Proven ability to lead technical teams and deliver complex solutions
8+ years of experience in software engineering, data engineering, or ML engineering.
3+ years delivering production AI/ML and/or GenAI solutions (RAG, agents, or similar)
Preferred :
Experience in financial services or other regulated industries.
Experience with DB2 or legacy-to-cloud data migration.

## Artifact metadata

- Resume: Not generated because the fit gate returned FAIL.
- Cover letter: Not generated because the fit gate returned FAIL.
- LinkedIn note: Not generated for a FAIL role.
- Google Drive used: No