AI & Data Analytics Lead

AutoRentals.com

21h

About The Position

We are looking for an AI & Data Analytics Lead to lead the company’s use of Artificial Intelligence, data, analytics, automation, and business intelligence. This is a hands-on role for someone who sees AI as a fundamental part of how a modern company should operate—not simply as another technology or tool. The goal of this role is to identify opportunities across the organization where AI can improve how we analyze data, make decisions, automate processes, build reporting, work in Excel, interact with systems, and operate the business. The ideal candidate combines strong data and BI skills with a deep understanding of modern AI tools and capabilities. You should be someone who naturally asks: “Why are we doing this manually, and how can AI make this better?” You will work across Finance, Operations, Marketing, Product, Technology, and other areas of the business to identify opportunities, build solutions, automate workflows, and create a more AI-enabled organization.

Requirements

5+ years of experience in Data Analytics, Business Intelligence, Data Science, AI, Automation, or a related field.
Strong hands-on experience with AI and generative AI tools and a demonstrated ability to apply them to real business problems.
Strong experience with Snowflake and SQL.
Strong experience with Tableau or another enterprise BI platform.
Advanced Excel skills, including Power Query, Power Pivot, data modeling, and automation.
Experience integrating AI with Excel, APIs, databases, BI tools, and business applications.
Ability to build practical AI solutions—not just experiment with ChatGPT or other AI tools.
Strong understanding of data structures, data modeling, analytics, and business intelligence.
Strong problem-solving skills and the ability to look at an existing process and identify how technology and AI can fundamentally improve it.
Ability to communicate complex technical concepts to non-technical users.
Comfortable working independently and taking projects from idea through implementation.
Strong business acumen and curiosity about how the entire business operates.
Generative AI / LLMs
AI workflow design and implementation
Snowflake
SQL
Tableau
Advanced Excel
Power Query / Power Pivot
Data modeling
Data visualization
Automation

Nice To Haves

Python
APIs
Office Scripts / VBA
Microsoft Copilot
OpenAI / ChatGPT APIs
Claude / Gemini or similar LLM platforms
dbt
Data pipelines / ETL
AI agents and agentic workflows
Machine learning / predictive analytics

Responsibilities

Lead the company’s practical adoption and implementation of AI across business functions.
Identify opportunities where AI can eliminate manual work, improve decision-making, increase productivity, reduce errors, and create new capabilities.
Evaluate emerging AI technologies, tools, platforms, and models and determine how they can be applied to the business.
Build and implement AI-powered workflows rather than simply recommending AI tools.
Integrate AI into existing business processes, systems, reporting, analytics, and workflows.
Develop AI solutions that allow employees to interact with company data using natural language.
Use AI to automate recurring analysis, reporting, research, data preparation, reconciliation, forecasting, and other manual processes.
Identify opportunities to use AI for anomaly detection, trend identification, forecasting, classification, summarization, and predictive analytics.
Develop repeatable AI solutions that can scale across departments.
Establish best practices for responsible and secure use of AI with company data.
Stay current with rapidly evolving AI capabilities and continuously identify new opportunities to apply them.
Transform how the company uses Excel by integrating AI into Excel-based analysis and workflows.
Develop AI-enabled Excel solutions that automate analysis, reporting, data manipulation, modeling, and repetitive tasks.
Connect Excel to Snowflake, APIs, BI platforms, and other data sources.
Leverage tools such as Microsoft Copilot, ChatGPT, Claude, APIs, Power Query, Power Pivot, VBA, Office Scripts, and other emerging technologies where appropriate.
Build workflows where users can ask questions of data and receive meaningful analysis without manually manipulating large datasets.
Automate recurring Excel reporting and eliminate unnecessary spreadsheet-based manual processes.
Identify opportunities to replace complex manual Excel processes with AI-enabled or automated solutions.
Use AI to fundamentally improve how the company accesses, analyzes, and understands its data.
Work extensively with Snowflake as a core data platform.
Write and optimize SQL queries and develop data models and datasets for analytics.
Use Tableau and other BI tools to develop dashboards and reporting.
Incorporate AI into dashboards and analytics so users can more easily understand and interact with information.
Develop natural-language or AI-assisted interfaces to company data where appropriate.
Use AI to identify trends, outliers, relationships, and insights that may not be obvious through traditional reporting.
Improve data quality and identify inconsistencies using automated and AI-assisted processes.
Partner with Technology and Engineering to improve data pipelines, integrations, APIs, and the overall data environment.
Partner with leadership and business teams to identify high-value analytical and AI opportunities.
Translate business problems into data- and AI-driven solutions.
Develop KPIs, dashboards, analytical models, forecasts, and decision-support tools.
Use AI to accelerate and improve forecasting, scenario analysis, budgeting, financial analysis, and business planning.
Create tools that allow leadership and business users to quickly answer questions using company data.
Move reporting from static historical information toward automated, predictive, and actionable intelligence.
Identify manual and repetitive processes throughout the organization that can be automated using AI, APIs, workflow tools, or traditional automation.
Build automated workflows that connect data, applications, and AI models.
Look beyond the existing process and determine whether AI can eliminate the process entirely or fundamentally redesign it.
Measure the time, cost, and efficiency improvements generated by AI initiatives.
Create scalable solutions rather than one-off experiments.