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Background
Data Science Consultant with expertise in economics, time series analysis, and Bayesian methods
I implement data science solutions for organizations across a range of industries through implementation of statistical analysis as well as more advanced machine learning methodologies.
My educational background is a Master’s degree in Economics. As such, much of my work has been in the domain of business intelligence; i.e. using machine learning technologies to develop solutions to a wide range of business problems. I most frequently work with Python and R.
My areas of interest include
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Classification and regression modelling of customer features to identify significant influencing factors on customer interest and sales.
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Interpretable machine learning to identify Diverse Counterfactual Explanations, e.g. what changes in features would turn a non-buyer into a buyer?
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Linear Mixed Effect Modelling with TensorFlow Probability to identify the impact of separate customer segments on overall sales data.
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Time series forecasting of sales data using tools such as ARIMA and Prophet.
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Use of Bayesian analysis to quantify effects of interventions, e.g. how do factors such as economic shifts or marketing campaigns affect customer interest for a product?
Are you working on a similar type of problem that you wish to discuss? If you feel that my expertise can be of help to you, feel free to e-mail me.
Frequently used techniques and technologies
ARIMA, AWS, InterpretML, Linux, MySQL, PostgreSQL, Prophet, PyMC3, Python, R, Shiny Web Apps, TensorFlow
Training Courses
Time Series Forecasting with Bayesian Modeling. LiveProject series produced for Manning Publications
- Course content includes modelling of time series shocks with Bayesian Dynamic Linear Modeling, modeling of posterior distributions with PyMC3, MCMC sampling with Prophet, and Structural Time Series Modeling with TensorFlow Probability.
TensorFlow 2.0 Essentials: What’s New. Video seminar produced for O’Reilly Media
- Illustrated use of eager execution and AutoGraph, as well as use of tf.keras for neural network modelling across classification, regression, and time series datasets.
Business Analytics with R — Statistics and Machine Learning. Video series produced for O’Reilly Media
- Illustrated use of data manipulation techniques, regression analysis and hypothesis testing, along with classification and regression-based machine learning techniques.