Job Description
Job Summary The Data Science Intern will support Columbia Forest Products by assisting in the development of predictive models, forecasting tools, and data‑driven insights that guide operational and strategic decision‑making. The intern will work with historical and real‑time datasets, help evaluate model performance, and support data science initiatives across operations, supply chain, forecasting, and business analytics.
This role is ideal for a student who enjoys turning data into actionable predictions, building statistical or machine‑learning models, and collaborating with business stakeholders.
Key Responsibilities Modeling & Forecasting
- Build, test, and validate forecasting models (time‑series, regression, machine learning) to support demand planning, production forecasting, and operational insights.
- Develop predictive models using Python or R for classification, regression, or anomaly detection tasks.
- Conduct exploratory data analysis to uncover patterns, trends, and drivers behind forecasting outputs.
Data Preparation & Analysis
- Clean, preprocess, and engineer features from structured and unstructured datasets.
- Perform statistical analysis to support forecasting accuracy and model stability.
- Apply data‑quality checks and troubleshooting to ensure datasets are complete, reliable, and usable.
Model Evaluation & Reporting
- Evaluate model performance using metrics such as MAPE, RMSE, MAE, precision/recall, or other relevant KPIs.
- Document methodologies, workflows, and experiment results.
- Create visualizations and reports that clearly communicate model insights to technical and non‑technical stakeholders.
Collaboration & Integration
- Partner with data engineers, analysts, and business teams to operationalize insights and models.
- Participate in data science reviews, brainstorming sessions, and forecasting discussions to refine model objectives.
- Support ongoing enhancements to existing models or forecasting tools based on stakeholder needs.
Qualifications - Currently pursuing a degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, or another quantitative field.
- Proficient with MS PowerPoint, Word and Excel
- Experience with statistical software like R (R Studio) or Python is not required but strongly preferred
- Experience with scenario modeling software (like Oracle Crystal Ball) or process simulators is nice to have
What You’ll Gain - Hands‑on experience building and validating predictive and forecasting models that support real business decisions.
- Exposure to enterprise data science workflows and tools used across manufacturing and corporate operations.
- Practical experience working with large datasets, advanced analytics, and cross‑functional teams.
- A strong foundation in applied forecasting and data‑science methods, preparing you for future roles in analytics, data science, or AI.
Details - Duration of internship will be from June 1st to August 14th.
- Paid up to 40 hours per week
- CFP’s goal is to promote academic, career and personal development
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