Data Scientist

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.

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.

Key Responsibilities

• Forecasting & Advanced Analytics
  • Lead the design, development, and optimization of forecasting models for:
  • Cash demand (branches, ATMs, retail locations, vaults)
  • 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.
• AI, ML, & LLM Enablement
  • 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.
• Data Quality, Governance & Model Risk
  • 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.
• Technical Leadership & Collaboration
  • Required Qualifications
  • 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.
  • Preferred Qualifications
  • 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

  Required Qualifications

  • 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.

  Preferred Qualifications

  • 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

  Ideal Candidates

  • Comfortable with ambiguity
  • Driven to elevate themselves by elevating others
  • Curious and life-long 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

  What Success Looks Like

  • Forecasting models that are accurate, explainable, and trusted by clients and internal teams.
  • AI and LLM use cases that measurably reduce operational effort and improve response quality.
  • Strong data quality visibility that proactively identifies issues before they impact forecasts.
  • Clear, well-documented models and methodologies that scale across clients and use cases.
  • A collaborative, high-impact partnership with engineering, product, and client teams.

  Benefits

  • Vacation and Sick Time (PTO) as well as Paid Holidays
  • Health & Dental Insurance
  • Vision Insurance
  • 401(k) Plan
  • Basic Life Insurance Plan
  • Voluntary Life Insurance Plan
  • Flexible Spending and Health Savings Account
  • Dependent Care Account
  • Industry-leading Training and Development

  Job Details

  Job Family: Exempt

  Pay Type: Salary