Lead Network Engineering

Description

We are seeking a seasoned Data Scientist with front-line operational and/or industrial engineering experience to turn complex data into actionable insights. In this role, you will enhance forecasting and predictive models to support tactical and strategic planning across multiple lines of business.

You bring expertise in time series forecasting, demand modeling, and machine learning, along with experience in supply chain, logistics, or transportation. Highly data-driven, you excel at identifying trends, integrating operational and customer insights, and developing accurate, stable, forward-looking forecasts that enable effective capacity planning and strong service performance.

Responsibilities

Demand Forecast

• Develop and implement predictive forecasting models to support both tactical and strategic planning


• Support Sales & Operations Planning (S&OP) by collaborating with operational teams to translate operational conditions, customer insights, and volume trends into forward-looking demand forecasts that drive resource planning, productivity, and service targets

Operational Data Analysis

• Analyze operational data to identify trends, patterns, and actionable insights


• Develop visualizations (charts, graphs, dashboards) to present insights to stakeholders


• Conduct custom analyses to answer specific business questions and support decision-making

Continuous Improvement

• Collaborate with internal and external experts to enhance existing processes


• Share best practices and contribute to a culture of continuous learning and improvement.

Education

• Bachelor’s or Master’s degree in Operations Research, Data Science, Computer Science, Engineering, Mathematics, Statistics, or a related field.

Experience

• 10 or more years of experience in data science, engineering, or analytics roles within supply chain, logistics, or transportation industries


• Experience with commercial optimization solvers such as CBC, IBM CPLEX, or Gurobi for solving mathematical optimization problems.


• Strong understanding of statistical analysis, predictive modeling, and machine learning (e.g., regression, decision trees, clustering, time series forecasting).


• Proficiency in Python or R, with experience using libraries such as Pandas, NumPy, Scikit-learn, TensorFlow, or PyTorch.


• Knowledge of SQL and experience in data extraction from databases and APIs.


• Strong skills in data visualization using tools like Matplotlib, Seaborn, Plotly, or Tableau.


• Exposure to cloud computing platforms such as AWS is a plus.


• Familiarity with Git, and GitHub for version control and collaborative coding.


• Excellent communication and teamwork skills.