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 will work with the implementation team including a Senior Scientist and Project Manager. Responsibilities and compensation will be commensurate with qualifications.
In this role, you will use statistical, econometric and machine learning methods to forecast and answer complex questions related to market forecasting. You should be passionate about challenging prediction problems, finding insights in data, and driving practical business impact for customers.
DecisionNext is a rapidly growing software company helping food, natural resources and chemicals companies make better decisions about sales, purchasing, and operations through sophisticated analytical tools.
What you'll do:
- You will build and own forecasting models for customer implementations across commodity industries from thermal coal to beef.
- Communicate model results and insights to customers in practical business terms.
- Research and implement time series prediction and forecasting methods.
- Write and interpret complex Python/R scripting for standard as well as ad hoc data analysis purposes.
Who you are:
- You have demonstrated ability to work collaboratively across different functions and influence senior business partners for data-driven decision making.
- You have a diverse skill set covering data analysis, statistical modeling, machine learning, and computing.
- Expertise in time series analysis or machine learning applied to forecasting is highly desired.
- You excel at communicating complex ideas to business partners.
Things we are looking for:
- Ability to process, manipulate, and analyze data independently in one or more frameworks (python, BI tools, Excel, etc.)
- Programming skills.
- Effective with customers
- Comfortable explaining concepts to non-scientists
- Familiar with time series modeling and forecasting methodology
- Knowledge of basic probability theory
- Master’s degree Economics, Statistics, Applied Math, Physics, Operations Research, Engineering or a related field, or 3+ years equivalent relevant work experience
- Demonstrated interest in commodities business
- Background in econometric analysis to relate supply and demand variables with price
- Familiarity with Bayesian parameter estimation