Good brand selection overshadowed by poor management and pay cuts - Recensione dipendente - Sales Representative presso Lithia & Driveway

1,0
23 mar 2026
Consiglia
Gradimento del CEO
Pronostico commerciale

Vantaggi

Good brand selection throughout Lithia company.

Svantaggi

In a time where cost of living is the most insane it’s ever been, expect continuous pay cuts because management doesn’t know how to do proper business and will use your pay to fix their bottom line. You will work for managers who have no business being in their positions and are as far from competent as you can imagine, but know the right people who plop them into higher up titles. Employee relations should be renamed to management protection because they brush offensive and inappropriate behavior under the rug with excuses and zero accountability. Growth powered by people is the biggest joke this company advertises, they have no concern about individuals and expect you to do more with less happily. They do not value sales acumen, you will just be a warm body no matter what you produce for the store you work for. Healthcare coverage, PTO and 401k benefits are absolutely abysmal for a company of this size. This company has one focus and you will pay for it at every turn: their share price.

Esplora altre recensioni su Lithia & Driveway

5,0
6 giu 2026
Consiglia
Gradimento del CEO
Pronostico commerciale

Vantaggi

Love what I do. Great team members.

Svantaggi

Very Hot on summer days.

5,0
15 mag 2026
Consiglia
Gradimento del CEO
Pronostico commerciale

Vantaggi

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.

Svantaggi

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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