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Lithia & Driveway

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beware of lithia/driveway - Collector Lithia & Driveway Employee Review

1.0
Feb 2, 2021
Recommend
CEO approval
Business Outlook

Pros

The benefits are okay as far as the automotive industry goes but in the grand scheme of benefits they’re not great. The discounts on cars is the only real pro.

Cons

I worked for lithia collections branch (driveway formerly known as Southern Cascade) for not even a month before I resigned. There were constant micro aggressions (racially charged, saying the “r” word to express anger, LGBT phobic etc). In an office of about 30 people I didn’t see one black person? The ways collectors and managers would talk about debtors was disgusting. It was such a negative, cold and dark environment. In all my years working collections- this was one of the hardest places to get up and go to work to in the morning. I would not recommend at all. If you’re looking for a call center position there’s plenty other ones that have professional management and at the very least neutral work environments that don’t suck the actual life out of you. Management was so catty and did not know how to actually lead or teach employees. I could go on and on. Overall- no.

Explore other reviews about Lithia & Driveway

5.0
Jun 6, 2026
Recommend
CEO approval
Business Outlook

Pros

Love what I do. Great team members.

Cons

Very Hot on summer days.

5.0
May 15, 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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