A typical day will involve collaborating with software engineers and business groups. Your work will involve utilizing Python and SQL to implement and improve upon demand forecasting algorithms, by testing model improvements, running computational experiments, and fine-tuning model parameters. Deployment of sophisticated multivariate algorithms will improve upon existing approaches and discovering new data sources to further build upon current capabilities. As a successful data scientist in demand forecasting, you would be an analytical problem solver who enjoys diving into data, is passionate about investigations and algorithms, and can credibly interact between technical teams and business partners. A key capability in the collaboration is to have users of the forecasts understand how it is built up, its expected behaviour and what is and is not considered, thus building trust in the models to drive usage. Requirements • Master's degree in a quantitative field such as Data Science, Statistics, Applied Mathematics, Physics, Computer Science or Engineering • At least 5+ years of relevant working experience in an analytical role involving data extraction, analysis and statistical modelling • Advanced skills with statistical/programming in Python and data querying languages (e.g., SQL, Hadoop/Hive, Scala) • Solid understanding of time-series forecasting techniques • Good hands-on skills in both feature engineering and hyperparameter optimization • Able to write clean and tested code that can be maintained by other software engineers • Able to clearly summarize and communicate data analysis assumptions and results