IT Data Analyst

Vital Tech SolutionsTaylor, MI

About The Position

The IT Data Analyst is responsible for transforming complex data into meaningful insights that support strategic business decisions. This role partners with both technical teams and business leaders to analyze historical and operational data, develop reporting solutions, and contribute to predictive and prescriptive analytics initiatives. The ideal candidate combines strong technical expertise with business acumen to identify opportunities for improved performance, efficiency, and profitability.

Requirements

  • Bachelor's degree in a quantitative discipline such as Computer Science, Statistics, Mathematics, Economics, Industrial Engineering, Operations Research, or a related field. Advanced degrees are a plus.
  • Strong proficiency in SQL for querying and manipulating relational databases.
  • Experience with business intelligence and data visualization platforms such as Tableau, Power BI, Amazon QuickSight, Sisense, or similar tools.
  • Working knowledge of Python and/or R for data analysis and statistical modeling.

Nice To Haves

  • Experience working with large-scale datasets using technologies such as Apache Spark.
  • Experience developing, validating, and deploying predictive analytics or machine learning models.
  • Demonstrated ability to move analytical solutions from proof-of-concept through production implementation.
  • Understanding of both classical (Frequentist) and Bayesian statistical methodologies.
  • Experience with time series analysis and forecasting techniques.
  • Familiarity with optimization methods, linear programming concepts, and tools such as CPLEX, Gurobi, or comparable optimization software.

Responsibilities

  • Collect, integrate, and prepare data from multiple internal systems and external sources for reporting, analysis, and modeling.
  • Analyze large datasets to identify trends, patterns, risks, and opportunities that drive informed business decisions.
  • Design, develop, and maintain reports, dashboards, and visualizations that provide actionable performance insights.
  • Collaborate with business stakeholders to understand analytical requirements and deliver data-driven recommendations.
  • Support the development, testing, and maintenance of predictive and prescriptive analytical models.
  • Ensure data quality, consistency, and accuracy across reporting and analytical processes.
  • Communicate analytical findings effectively to both technical and non-technical audiences.
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