- Translate business challenges into analytical approaches and technical solutions
- Develop statistical and machine learning models supporting initiatives such as demand forecasting, staffing optimization, and operational planning
- Build time series models and predictive analytics solutions to support operational and commercial decision-making
- Prepare, clean, and transform raw data into structured datasets suitable for statistical modeling and analysis
- Identify and incorporate new data sources and variables to improve model performance and predictive accuracy
- Translate model outputs into operational insights and measurable business KPIs
- Develop visualizations and reporting tools that communicate model results and insights to business stakeholders
- Integrate analytical outputs into operational workflows and decision-making processes
- Partner with cross-functional teams across operations, technology, and analytics to prioritize and execute data initiatives
- Build relationships with internal and external data and technology partners to enhance analytics capabilities
Requirements
- 1-3 years of experience in a data science, machine learning, or advanced analytics role
- Undergraduate degree in Data Science, Statistics, Mathematics, Computer Science, Engineering, or another quantitative field
- Experience applying statistical modeling and machine learning techniques to real-world business problems
- Strong programming skills in Python (e.g., Pandas, scikit-learn) and SQL
- Experience with time series forecasting and machine learning models
- Familiarity with data visualization and business intelligence platforms such as Power BI or Tableau
- Experience applying diverse analytical methodologies to solve technical and operational challenges
- Ability to communicate complex analytical concepts clearly to both technical and non-technical stakeholders
- Strong collaboration skills with the ability to work both independently and within cross-functional teams
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