Things are looking up - Recensione dipendente - Financial Analyst presso Lithia & Driveway

4,0
10 gen 2012
Consiglia
Gradimento del CEO
Pronostico commerciale

Vantaggi

Everybody in the corporate office seems very nice including the senior management. Middle management is actively working to improve employee engagement. The company has been doing well as the economy recovers and will soon open a new HQ building that will help revitalize the downtown area. They seem to screen new hires well for apptitude and personality fit. I think good things are ahead for Lithia.

Svantaggi

There is a lack of focus and not a clear definition of who Lithia is and why a consumer would want to buy a car from them versus a competitor. Customer service is a low priority as long as cars continue to be sold. The working environment in the dealerships is poor and full of turnover. Generally speaking, pay is low and benefits are minimal and expensive until you hit management. The 401K match is dismal. General Managers make a good living and are the heros of the company. Why can't everyone be a hero?

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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