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Mid-level generalist data role with broad, in-demand tooling attracts many qualified applicants.
Analytics and data engineering skills are broadly transferable across industries and roles.
Multiple mandatory analytics platforms and tooling required, but broad experience range allows flexibility.
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Design, develop, and deploy predictive and analytical models to solve business problems and support strategic decision-making.
Build interactive dashboards, reports, and visualizations for business stakeholders using BI tools like Power BI, Tableau, or Looker.
Develop scalable data analytics solutions with cloud and big data platforms, collaborating with cross-functional teams to translate business requirements into analytical workflows.
3–11 years of professional experience in Data Science, Business Analytics, or Advanced Analytics.
Bachelor's degree in Data Science, Computer Science, Statistics, Mathematics, Information Technology, or a related field.
Strong proficiency in Python or R, and experience with Databricks or Dataiku for data science and model deployment.
Experience using Power BI, Tableau, or Looker for dashboard development and visualization; knowledge of SQL and data warehousing with BigQuery or Snowflake.
Experience working in Agile/Scrum environments, indicating adaptability to iterative development and collaboration.
Hands-on expertise in end-to-end analytical workflows including statistical analysis, predictive modeling, and machine learning.
Familiarity with cloud analytics platforms (AWS, Azure, or GCP) and interest or experience in MLOps and AI-driven analytics to enhance deployment and operationalization.