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* Development and maintenance of machine learning models and data-driven insights for use in the company's services and decision support
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* Data collection, data cleaning and structuring of larger datasets for various internal and external sources
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* Contribute to shaping the company's strategy in data analysis, machine learning and artificial intelligence
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* Visualization and communication of findings to technical and non-technical stakeholders
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* Developed the business critical moisture classification deep learning model for inside-roof into® sensors using Long-Short Term Memory (LSTM)
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* Developed an algorithm to predict sudden spikes in into® sensor's moisture readings which provided early alarms / notifications possibilities
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* Developed an ensamble denoiser / smoothner for relative humidity readings from into® sensors using median and kalman filters resulting in user interpretable charts in the into® Control System (iCS)
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* Improved business critical data pipelines deployed in AWS such as IOT core integration with SQS and all python based lambda functions
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* Developed observability dashboards in AWS spanning different operational business needs such as drift in ML model and RDS and Lambda performance.
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* Developed internal proof-of-concepts to retriving updated customer invoicing data in hubspot for the sales department
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