Work collaboratively with the Statistical Forecast Experts to implement and explore the performance of different machine learning algorithms (e.g. LSTM, Random Forest) applied on time series data:
- Simulate: Create a multiple what-if scenarios coming from the different statistical models.
- Measure: Use key forecast metrics for model selection.
- Refine: fine-tuning based on the results of simulations and accuracy measured.
Conduct data analysis among large amounts of raw data (e.g. industry, historical sales, forecasts) and dig out opportunities to improve the statistical forecast.
Aid in root cause analysis to identify forecast variances.
The candidate should be currently enrolled in a Bachelor/Master degree in data science, applied mathematics, statistics, econometrics or any related field.
English is a must.
Any other language can be an advantage.
Skills & Qualifications
- Initial/Basic knowledge of data science packages - Python (e.g. NumPy, Pandas, Statsmodels, SciKit-Learn, TensorFlow) and R (e.g. Tidyverse, forecast, Keras, H20, tsibble, randomForest)
- Good knowledge of Microsoft Office with a focus on Excel
- Team spirit
- Good communication skills
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