Data Scientist

Loomis•Suwanee, GA

About The Position

The Data Scientist position is in 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 to dedication to client results.

Requirements

Unrestricted authorization to work in the U.S. without the need for employer sponsorship.
6+ years of professional experience in data science, machine learning, or advanced analytics
Advanced proficiency with Python and data science libraries (e.g., pandas, NumPy, scikit-learn, TensorFlow/Torch)
Strong SQL skills and experience working with messy, incomplete, high-volume operational data
Well-rounded background in data science methods (e.g., supervised and unsupervised learning, anomaly detection, time series forecasting, survival analysis, simulation, optimization, causal analysis)
Familiarity with metric design
Demonstrated delivery of products that influenced business decisions
Experience collaborating with engineering teams on model deployment and monitoring.
Proven ability to communicate complex concepts clearly and effectively.

Nice To Haves

Experience in FinTech, banking, payments, retail cash management, or operations
Experience identifying high-value data science opportunities in operational businesses
Hands-on LLM development experience
Familiarity with data quality and model governance frameworks
Comfortable with ambiguity
Driven to elevate themselves by elevating others
Curious and lifelong learners
Able to identify valuable problems before being asked
Pragmatic rather than purely academically focused
Capable of explaining very technical ideas to non-technical stakeholders
Willing to challenge their own and others’ assumptions with evidence
Open to changing their mind when presented with new evidence

Responsibilities

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.