Data Scientist

Epicor

Data Scientist turning business data into insights for Epicor’s ERP software. Developing analytical models and customer-facing analytics with data and product teams.

Posted 8/4/2026full-timeLehi • 🇺🇸 United StatesJuniorMid-Level💰 $94,000 - $151,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in data analysis, statistical modeling, and machine learning, with proficiency in Python and SQL. Capable of collaborating with cross-functional teams to develop and implement data-driven solutions that address business needs.

Highest-signal resume keywords

Data AnalysisStatistical ModelingPython ProgrammingSQL ProficiencyMachine Learning

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data ScienceStatistical MethodsModel EvaluationData CleaningData TransformationPattern IdentificationHypothesis TestingAnalytical Problem-SolvingData VisualizationCloud-Based AI Services

Soft Skills

Strong Communication Skills

Tools & Technologies

PandasNumPyScikit-LearnPower BITableauMicroStrategyAI ToolsMachine Learning Frameworks

Industry Keywords

Data WarehousingData Processing WorkflowsDistributed Data ProcessingBusiness Use CasesAnalytics Capabilities

Tech Stack

Tools & technologies

CloudNumpyPandasPythonScikit-LearnSQLTableau

About the role

Key responsibilities & impact

Analyze structured and unstructured datasets to identify trends and answer business questions
Develop, test, and refine statistical and analytical models
Contribute to analytics capabilities aligned with product roadmaps, customer needs, and business use cases
Partner with data warehouse engineers, data engineers, product teams, subject-matter experts, and stakeholders on end-to-end analytical solutions
Prepare, clean, transform, and validate data for analysis, modeling, reporting, and experimentation
Evaluate new data sources and analytical methods
Document analytical approaches and communicate findings
Support deployment and ongoing improvement of data science solutions

Requirements

What you’ll need

2–4 years’ experience in data science, analytics, statistical modeling, or a related field, including relevant internships, academic projects, or applied professional experience
Working knowledge of Python and SQL
Understanding of statistical methods, model evaluation, and analytical problem-solving
Experience identifying patterns, testing hypotheses, and communicating actionable findings from datasets
Familiarity with database concepts, data warehousing, or data-processing workflows
Strong written and verbal communication skills
Bachelor’s degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, Economics, or a related field, or equivalent practical experience
Coursework, academic projects, certifications, or early professional experience involving artificial intelligence, machine learning, or generative AI
Experience with pandas, NumPy, scikit-learn, or similar analytical tools
Exposure to AI or machine-learning tools, frameworks, APIs, or cloud-based AI services
Experience applying AI or machine learning to practical business, product, or customer use cases
Exposure to R or other data science and statistical tools
Experience with Power BI, Tableau, or MicroStrategy
Familiarity with cloud data platforms, distributed data-processing environments, or production analytics workflows

Benefits

Comp & perks

Comprehensive health and wellness benefits
Opportunities for mentorship
Continuing education
Focused career goal setting
Free LinkedIn Learning licenses
Mentoring Program
Inclusive, global workplace
Work-life balance policies encouraging time off
Support for international relocations and permanent residency processes