Senior Manager, Analytics AI Enablement

First Help Financial•Needham, MA

6d•Remote

About The Position

First Help Financial (FHF) is a fast-growing and culturally diverse company in the U.S. We provide auto loans to the underserved and care for our customers and partners with exceptional service. Through flexible financing options and tri-lingual support, we offer consumers an easier way to finance their first car. We lend to and support our portfolio which has consistently grown 30%+ each year over the last nine years. Here you will find hard-working colleagues who come from over 20 countries. We hold ourselves to the highest standards of professionalism but also enjoy our work. Our culture and benefits are geared towards making you successful in life and comfortable at work. Your Title: Senior Manager, Analytics AI Enablement. Your Location: Remote/Anywhere in the US. You Report To: Associate Director, Enterprise Data. Compensation: $157,000 or more depending upon experience plus a bonus. Learn more about our awesome Analytics team! About the Opportunity: First Help Financial, recently voted as a " Great Place to Work " for the fifth year in a row is seeking a Senior Manager, Analytics AI Enablement partners closely with Analytics/Insights and Data Engineering to deliver AI-powered analytics, accelerating insight generation, improving metric consistency, and enabling trusted self-serve decision support. This role builds scalable foundations (AI-ready structured and unstructured data plus a governed semantic layer for Cortex Intelligence) and drives 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

Analytics AI Strategy & Roadmap: 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.
Analytics Data Foundations & Semantic Layer (Cortex Intelligence): 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.
AI-Powered Analytics Delivery & Adoption: 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.