Data Scientist

Job Description

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!

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.

The Data Scientist will play a critical role in designing, scaling, and operationalizing advanced analytics and machine learning solutions across the company’s Fin Tech 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”.

• Lead the design, development, and optimization of forecasting models for cash demand (branches, ATMs, retail locations, vaults) and labor and operational workload forecasting.
• Apply and evaluate time-series, probabilistic, and machine-learning techniques to improve forecast accuracy and stability.
• Own model performance monitoring, drift detection, recalibration strategies, and continuous improvement.

• Design and implement LLM-based use cases to support internal teams (e.g., support, implementation, operations).
• Develop approaches for prompt engineering, evaluation, and governance of LLM outputs.
• Partner with engineering to integrate AI capabilities into production SaaS workflows.
• Define metrics to measure effectiveness, accuracy, and operational impact (ROI) of AI solutions.

• Establish data quality frameworks to detect anomalies, gaps, and integrity issues across large transactional datasets.
• Define validation rules, thresholds, and scoring mechanisms to support data confidence and forecast reliability.
• Contribute to model documentation, explainability, and governance practices aligned with financial services expectations.
• Support audit, compliance, and client due diligence inquiries related to data and models.

• 6+ years of professional experience in data science, machine learning, or advanced analytics.
• Advanced proficiency with Python and data science libraries (e.g., pandas, Num Py, scikit‑learn, Tensor Flow/Torch).
• Strong SQL skills and experience working with messy, incomplete, high-volume operational data.
• Well‑rounded background in data science methods (supervised and unsupervised learning, anomaly detection, time series forecasting, survival analysis, simulation, optimization, causal analysis).
• Familiarity with…