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Loomis Armored US, LLC
Data Scientist
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What they do
A Data Scientist utilizes skills and experience to systematically answer questions using data to provide actionable recommendations. Commonly utilizes advanced statistical analysis and machine learning techniques. Common responsibilities also include data cleaning and data management.
$106,013 / year median in Georgia
+22% projected growth
Job Description
Data Scientist 2.6 2.6 out of 5 stars 3325 Paddocks Parkway, Suwanee, GA 30024 Full-time Loomis Armored
US, LLC 2,703
reviews Full-time With a network of nearly 200 branches, Loomis armored transportation, cash management centers, and cash inventory vaults keep cash flowing throughout financial institutions and retail businesses across the US. Loomis prides itself on providing employees with opportunities for career advancement and job satisfaction. In fact, many of our company's managers, vice presidents, and corporate executives started out in the branches as driver/guards and tellers. Our work can be challenging, but the thousands who have stayed with our company for decades will tell you that if you have the desire to learn and the drive to succeed, Loomis is the place to be. Come join our team! Summary The position of Data Scientist is for the Logicpath division within Loomis. We are a team of tech-savvy cash inventory management experts passionate about helping financial institutions succeed. We provide a collaborative and supportive environment that values the participation and contribution of all employees. We are looking for people who want to be challenged, solve complex problems, and feel connected to a larger purpose. Our mission-focused team, collaborative nature, and commitment lead dedication to client results. Function The Data Scientist will play a critical role in designing, scaling, and operationalizing advanced analytics and machine learning solutions across the company's FinTech platforms. This role will lead complex forecasting initiatives, develop AI-driven use cases (including LLM-enabled support tools), and establish strong data quality and model governance practices. This position requires a hands-on technical leader who can translate real-world operational and financial problems into robust, production-ready data science solutions, while partnering closely with engineering, product, implementation, and client-facing teams. The ideal candidate combines strong statistical and machine learning expertise with practical engineering ability and a track record of delivering production-grade solutions in environments where communication, business processes, data quality, and operational constraints matter as much as model performance. This very technical person is capable of thinking in terms of "problem- > solution
- > product
- > value", not just "models".