Data Science Analyst

Shamrock Foods•Phoenix, AZ

10d

About The Position

The Data Science Analyst is responsible for using data science, machine learning, statistical modeling, and advanced analytics to solve complex business problems across Supply Chain and Operations. This role works at the intersection of data, technology, and business operations to develop predictive insights, analytical models, and decision-support tools that improve planning, efficiency, cost management, and service performance. The Data Science Analyst partners with business leaders, operations teams, IT, data engineering, and analytics stakeholders to identify high-value use cases, build and test analytical models, and translate technical outputs into actionable business recommendations. This role is well suited for a candidate who has strong technical skills but also enjoys applying those skills to practical operational challenges.

Requirements

Bachelor’s degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, Business Analytics, Economics, Operations Research, Supply Chain, or a related field required.
2-5 years of experience in data science, analytics, statistical modeling, machine learning, business intelligence, or related analytical roles.
Experience applying data science methods to real-world business or operational problems.
Proficiency in Python, R, SQL, or similar analytical programming languages.
Familiarity with machine learning libraries and methods such as scikit-learn, pandas, NumPy, regression models, classification models, clustering, time series forecasting, or optimization techniques.
Strong understanding of statistics, probability, data modeling, feature engineering, and model evaluation.
Ability to work with structured and unstructured data from multiple systems.
Ability to explain technical concepts to non-technical business stakeholders.
Strong problem-solving, critical thinking, and analytical reasoning skills.
Strong communication and data storytelling skills.
Ability to balance technical depth with practical business applications.
Strong attention to detail, data quality, and model reliability.

Nice To Haves

Master’s degree in Data Science, Analytics, Statistics, Operations Research, Computer Science, Industrial Engineering, or related discipline preferred.
Experience building predictive models, forecasts, or machine learning prototypes preferred.
Experience with Power BI, Tableau, Databricks, Azure Machine Learning, Snowflake, or similar platforms preferred.

Responsibilities

Develop, test, and validate predictive models and machine learning solutions for Supply Chain and Operations use cases.
Apply statistical modeling, forecasting, classification, regression, clustering, optimization, and other data science techniques to solve business problems.
Support use cases such as demand forecasting, labor forecasting, inventory risk, transportation optimization, order volume prediction, customer behavior trends, productivity analysis, and operational exception detection.
Develop and deploy anomaly detection models to identify operational exceptions, service disruptions, inventory irregularities, equipment failures, process deviations, and emerging business risks before they impact performance.
Support transportation and logistics optimization initiatives by applying advanced analytics and machine learning techniques to improve route efficiency, reduce fuel consumption, optimize network flows, and enhance service performance.
Clean, transform, structure, and analyze large datasets from multiple business systems.
Perform exploratory data analysis to identify relationships, trends, anomalies, and improvement opportunities.
Collaborate with business stakeholders to understand operational processes, pain points, and decision-making needs.
Translate business problems into data science questions, analytical methods, and measurable outcomes.
Evaluate model accuracy, performance, stability, and business value.
Partners with data engineering and IT teams to access, prepare, and improve data sources needed for modeling and analysis.
Create dashboards, visualizations, and presentations to communicate model outputs and recommendations.
Document model logic, assumptions, data sources, limitations, and business applications.
Support the deployment, monitoring, and ongoing refinement of analytical and machine learning solutions.
Stay current on data science, machine learning, AI, and analytics methods that may benefit the business.
Other duties as assigned.