Finance Analytics & AI Engineer

Slate Auto

About The Position

The Finance Analytics & AI Engineer is a senior individual contributor role based on the Commercial FP&A team, with scope to support the broader Finance organization. This role is the foundational technical hire responsible for architecting and building the reporting analytics and AI-driven automation that powers commercial decision making. A defining feature of this role is helping carry Finance reporting and analytics through Slate’s planned ERP migration from NetSuite to SAP (targeted for early 2027), ensuring data models, pipelines, and reporting remain stable and accurate across the transition. In the near term, NetSuite remains the system of record while SAP is stood up, so you should be comfortable building on NetSuite today and designing for portability to SAP.

Requirements

8+ years in FP&A, Business Intelligence, Data Analytics, or related fields — or equivalent demonstrated capability across FP&A, data engineering, and AI
Strong finance acumen (P&L, gross margin, unit economics, forecasting)
Advanced expertise in BI tools (Tableau, Power BI, Looker, etc.)
Experience working with ERP/EPM systems (NetSuite, SAP, and Adaptive Planning preferred)
Strong SQL and data modeling capabilities
Experience building data pipelines and working with data warehouses/lakes
Familiarity with Python or similar for analytics and automation
Experience integrating multiple data sources into a unified reporting layer
AI & Automation Experience (Required): Demonstrated hands-on experience building and deploying AI/ML models, agents, and analytics workflows in production (not experimental or coursework only)
Working proficiency with LLMs and agent-based architectures
Experience using AI tools to automate reporting, forecasting, or analysis
Strong interest in building AI-driven finance capabilities from the ground up
Mindset: Builder mentality: thrives in ambiguous, fast-moving environments
Highly analytical with strong attention to detail
Strong communicator who can translate data into business insights
Operates with urgency and a bias toward action

Responsibilities

Own the Finance Data & Intelligence Layer: Architect and scale Slate’s Finance reporting and analytics ecosystem across all Finance functions — including Commercial and Corporate FP&A, Accounting/Controllership, Treasury, and Procurement — as well as partner organizations such as Product Development
Design and maintain robust data models that integrate the ERP (NetSuite today, migrating to SAP by early 2027), EPM (Adaptive Planning), and operational systems
Build scalable data pipelines and layers to enable self-service analytics through AI agents
Lead Finance AI & Automation Strategy: Define and execute Slate’s Finance AI roadmap, including: AI-powered variance analysis (PVM, BvA/FvA), Automated forecast updates and anomaly detection, Inventory optimization, Natural language query tools for business users
Build and deploy AI agents that: Connect to finance and operational datasets, Enable leaders to query performance (e.g., SKU-level GM trends, pricing impacts), Automate recurring Finance workflows
Build Best-in-Class Dashboards & Reporting: Develop executive-ready dashboards and reporting across: Gross margin by product, channel and unit economics, Opex & Capex forecasting & actuals reporting, Sales, inventory, and take rate analyses, Plant KPI reporting (manufacturing cost, throughput, and operational performance), Business performance dashboard consolidating company-wide financial and operational KPIs for leadership, Sales KPI tracking (bookings, deliveries, and channel performance)
Partner Finance and Commercial stakeholders to standardize KPIs and reporting definitions
Deliver real-time insights for leadership (including Board-level materials)
Drive Advanced Analytics & Decision Support: Develop models for: Pricing optimization and margin expansion, Demand forecasting and inventory planning, Scenario modeling and long-range planning
Enable a Self-Service Data Culture: Build tools that allow non-finance stakeholders to access and interpret financial data
Implement data governance and output validation into AI models
Train business partners on dashboards, tools, and AI capabilities
Reduce manual reporting and elevate the organization toward real-time, insight-driven decision making