Modeling Scientist is the key role executing the math models behind DecisionNext software implementations, with customers across multiple commodities from agribusiness & food to mining & natural resources. Scientists work with the Customer Success team and the Sales team. Responsibilities and compensation will be commensurate with qualifications. In this role, you will use statistical, econometric and machine learning methods for forecasting and risk modeling. You should be passionate about challenging prediction problems, finding insights in data, and driving practical business impact for customers.

What you’ll do:

• You will build and own forecasting models for customer implementations across commodity industries such as meats, dairy or iron ore.
• Communicate model results and insights to customers in practical business terms.
• Research and implement methods for time series prediction and risk quantification.
• Write and interpret complex Python scripting for standard as well as ad hoc data analysis purposes.

Who you are:

• You have demonstrated ability to work collaboratively across different functions. A true team player.
• You have a diverse skill set covering data analysis, statistical modeling, machine learning, and computing.
• You have a passion for using/developing models to solve complex problems. Interest in commodities is a plus.
• You excel at communicating complex ideas to business partners for data-driven decision making.

Things we are looking for:

• Master’s degree/PhD in a quantitative field (Econometrics, Mathematics, Physics or similar).
• A good understanding of Python.
• Comfortable explaining concepts to non-scientists.
• Familiar with time series modeling and forecasting methodology.
• Advanced knowledge of probability theory.

Bonus Points:

• Interest in Food & Agriculture.
• Computing in AWS.
• Scikit-learn.

Location: Remote OK.