Rakshitha Godahewa
Forecasting Lead at Xtreme Soft Solutions
Melbourne, Victoria, Australia
Summary
👤
Senior🎓
Top SchoolRakshitha Godahewa is a Forecasting Lead based in Melbourne with 11 years of industry experience combining software engineering and advanced time-series research to deliver production-ready forecasting solutions for the energy sector. With a PhD in Time Series Forecasting and Machine Learning from Monash University and postdoctoral work that partnered with Meta and Tokopedia, she bridges cutting-edge research—publishing and peer-reviewing for top venues—with hands-on engineering using R, Python and scalable software practices. She has led forecasting science roles at jahan.ai and now AusNet while maintaining software development roots from senior engineering positions, enabling her to translate complex models into reliable operational systems. Known for ensemble and global modelling expertise, she also trains industry professionals and has a track record of industry collaborations (Honeywell, Tokopedia) that drive measurable business impact.
11 years of coding experience
7 years of employment as a software developer
BCS, The Charted Institute for ITSri Sumangala Girls' SchoolGateway School of ComputingInternational Computer Driving LicenseInstitute of Western Music & SpeechAssociated Board of the Royal Schools of MusicB.Sc. Engineering (Hons), Computer Science and Engineering, First Class, 4.01/4.2 Grade Point Average, B.Sc. Engineering (Hons), Computer Science and Engineering, First Class, 4.01/4.2 Grade Point Average at University of MoratuwaDoctor of Philosophy - PhD, Time Series Forecasting and Machine Learning, Doctor of Philosophy - PhD, Time Series Forecasting and Machine Learning at Monash University
English, Sinhala, Tamil
Stackoverflow
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Github Skills (17)
anomaly-detection10
arima10
data-mining10
time-series10
recurrent-neural-networks10
datasets10
forecasting10
time-series-forecasting10
ai10
scikit-learn10
forecast10
data-science9
machine-learning9
tsibble9
lasso-regression9
Programming languages (2)
Github contributions (5)
rakshitha123/WeeklyForecasting
Jul 2020 - Feb 2022
This repository contains the experiments related with a new baseline model that can be used in forecasting weekly time series. This model uses the forecasts of 4 sub-models: TBATS, Theta, Dynamic Harmonic Regression ARIMA and a global Recurrent Neural Network (RNN), and optimally combine them using lasso regression.
Contributions:32 commits, 15 pushes, 1 branch in 1 year 7 months
arimalasso-regressionrecurrent-neural-networkstime-series
rakshitha123/SETAR_Trees
Mar 2021 - Dec 2022
This repository contains the experiments related to a new and accurate tree-based global forecasting algorithm named, SETAR-Tree.
Contributions:26 commits, 27 pushes, 1 branch in 1 year 9 months
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