Data Scientist

Data ScientistThe 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.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".Key ResponsibilitiesForecasting & Advanced AnalyticsAI, ML, & LLM EnablementData Quality, Governance & Model RiskTechnical Leadership & CollaborationRequired Qualifications6+ years of professional experience in data science, machine learning, or advanced analyticsAdvanced 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 dataWell-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 designDemonstrated delivery of products that influenced business decisionsExperience collaborating with engineering teams on model deployment and monitoring.Proven ability to communicate complex concepts clearly and effectively.Preferred QualificationsExperience in FinTech, banking, payments, retail cash management, or operationsExperience identifying high-value data science opportunities in operational businessesHands-on LLM development experienceFamiliarity with data quality and model governance frameworksIdeal Candidates are:Comfortable with ambiguityDriven to elevate themselves by elevating othersCurious and lifelong learnersAble to identify valuable problems before being askedPragmatic rather than purely academically focusedCapable of explaining very technical ideas to non-technical stakeholdersWilling to challenge their own and others' assumptions with evidenceOpen to changing their mind when presented with new evidenceWhat Success Looks LikeForecasting 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 clientBenefits:Vacation and Sick Time (PTO) as well as Paid HolidaysHealth & Dental InsuranceVision Insurance401(k) PlanBasic Life Insurance PlanVoluntary Life Insurance PlanFlexible Spending and Health Savings AccountDependent Care AccountIndustry-leading Training and DevelopmentLoomis is an Equal Opportunity Employer and Drug Free Workplace. Qualified applicants will receive consideration for employment without regard to their race, color, religion, national origin, sex, sexual orientation, gender identity, protected veteran status or disability.