Senior Manager, Analytics AI Enablement

First Help Financial•Needham, MA

1d•Remote

About The Position

First Help Financial (FHF) is seeking a Senior Manager, Analytics AI Enablement to partner closely with Analytics/Insights and Data Engineering to deliver AI-powered analytics. This role aims to accelerate insight generation, improve metric consistency, and enable trusted self-serve decision support. The position involves building scalable foundations, including AI-ready structured and unstructured data, and a governed semantic layer for Cortex Intelligence. It also focuses on driving the adoption of AI-assisted analysis, storytelling, and experimentation with strong governance and measurable impact.

Requirements

Bachelor’s degree in computer science, data science, statistics, economics, or a quantitative field.
10+ years across analytics/data/AI, including 3+ years leading cross-functional analytics programs.
Strong SQL and Python (or similar) and experience enabling analytics consumption via governed semantic layers/metric definitions; comfortable with structured and unstructured data.
Experience building analytics products (dashboards, KPI frameworks, experimentation readouts) and using AI to accelerate analysis, insight generation, and storytelling.
Executive communication and stakeholder management; translating analytical and AI concepts into clear decisions and roadmaps.
Proven change in leadership and adoption enablement (training, governance) delivering measurable improvements in time-to-insight, decision quality, or risk management.

Responsibilities

Partner with Analytics/Insights leaders to identify, size, and prioritize AI-assisted analytics opportunities (e.g., faster analysis, improved forecasting, automated anomaly detection, and narrative insights).
Own the roadmap for AI adoption within the analytics function, including milestones, dependencies, governance checkpoints, and measurable outcomes.
Establish an analytics use-case intake process and success metrics to track accuracy/quality, time-to-insight, stakeholder adoption, and incremental insight lift.
Partner with Data Engineering to deliver analytics-ready datasets (structured and unstructured) with strong quality, lineage, and documentation to support trusted analysis.
Develop and govern the Cortex Intelligence semantic layer to standardize entities, dimensions, metrics, and business logic for consistent reporting and AI-assisted insights.
Enable retrieval and grounding over approved analytical knowledge (e.g., metric definitions, reporting standards, experiment readouts, business playbooks) with metadata and access standards; implement guardrails (citations, evaluation) to reduce hallucinations.
Standardize monitoring for data quality and AI-assisted insight outputs (e.g., drift, stability, and reproducibility); partner on governance, controls, and recurring business reviews.
Lead cross-functional delivery (within Analytics and with Data Engineering) for prioritized AI analytics use cases from requirements through launch and iteration (e.g., insight copilots, automated commentary, root-cause exploration, and anomaly detection).
Drive adoption through enablement (training, office hours, playbooks) and implement safe-response standards for analytics outputs (approved sources, citations as needed, confidence thresholds, and human review/escalation).
Enable AI/ML for analytical modeling (segmentation, forecasting, propensity/response, experimentation support) with performance monitoring, documentation, and governance.
Partner with analytics stakeholders to embed AI-assisted insights into reporting and decision cadences (dashboards, QBRs, and executive readouts) to improve speed and clarity of decisions.