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Lithia & Driveway

Is this your company?

Doesn't care about customers or employees - Anonymous employee Lithia & Driveway Employee Review

1.0
4 May 2016
Anonymous employee
Recommend
CEO approval
Business outlook

Pros

Got none, no pros to working for this money hungry company.

Cons

Personal experience at one of their dealerships; They don't care about customer service/their clientele or they're employees. They over work employees for terrible pay, expect employees to do the job of multiple people and don't hire anyone. They allow the dealership to fall and crumble and become so understaffed. All they care about is money and numbers. I would have stayed at my dealership forever, I wanted to build a career out of it, but to be one person doing the job of multiple people then getting made fun of by the Lithia appointed general manager for "scrambling" while he sat in his office yelling at people for why there are angry customers instead of helping solve the problem, that was too much. Terrible company, if they take over your dealership, get out and get out quick.

Explore other reviews about Lithia & Driveway

5.0
6 Jun 2026
Recommend
CEO approval
Business outlook

Pros

Love what I do. Great team members.

Cons

Very Hot on summer days.

5.0
15 May 2026
Recommend
CEO approval
Business outlook

Pros

Strong exposure to real business data from automotive sales, finance, inventory, and customer operations. Opportunity to work on high-impact analytics that directly affect dealership performance and revenue. Large-scale company with many datasets and business units, which is good for learning business intelligence and predictive analytics. Growing digital transformation initiatives can provide opportunities to work with modern analytics tools and automation.

Cons

Legacy systems and fragmented data sources may make data cleaning and integration challenging. Traditional corporate structure can sometimes slow down decision-making or the implementation of data-driven ideas. Work may lean more toward reporting/dashboarding than advanced machine learning, depending on the team. Stakeholders may prioritize quick operational insights over long-term data science experimentation. A high-pressure retail environment can lead to tight deadlines and rapidly changing priorities.

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