06/06/2023 updated
20 % partially available
Data Scientist
United Kingdom
United Kingdom
PhD Computational and statistical ecology; MRes Modelling Biological Complexity; MSc Natural Computation; BSc Cognitive ScienceJava (Programming Language)JavaScriptAgile MethodologyArtificial IntelligenceAmazon Web ServicesAnalytical ThinkingData AnalysisEnglishGermanProblem SolvingPython (Programming Language)MATLABMachine LearningNode.jsNumPy
Senior data scientist with strong technical capabilities, problem-solving and team working skills. Ten years’ experience in leading advanced data analytics and machine learning projects. By bringing together a strong academic background, analytical mindset, and comprehensive range of technologies, I deliver solutions to real-world business challenges.
Looking for part-time contracting opportunities, remote or in the Oxford area.
Fluent full professional English and native German speaker.
#keywords
agile, Amazon Web Services (AWS), artificial intelligence (AI), data analytics, data science, Java, Javascript/NodeJS, MatLab, machine learning (ML), Python (Matplotlib, Numpy, Pandas, SciKit-learn, XGBoost).
Looking for part-time contracting opportunities, remote or in the Oxford area.
Fluent full professional English and native German speaker.
#keywords
agile, Amazon Web Services (AWS), artificial intelligence (AI), data analytics, data science, Java, Javascript/NodeJS, MatLab, machine learning (ML), Python (Matplotlib, Numpy, Pandas, SciKit-learn, XGBoost).
Languages
GermanNative speakerEnglishFluent
Project history
Mobile device analytics
As part of an agile development team, he worked on a cloud-based big-data software-as-a-service
product. He had the lead responsibility for data analytics, machine learning and statistical methods .
Projects (selected)
* Drop detection : Detecting drops of mobile devices with random forest/gradient boosted decision
trees trained on device accelerometer data.
* Anomaly detection : Learning the variability of device performance measures for a population of
mobile devices using a Bayesian statistical model to detect changes in device performance after
an application update.
* WiFi location : Learning locations of Wifi access points based on their interaction with mobile
devices and using the learnt location to warn about misplaced mobile devices.
* Battery health thresholds : Optimising battery replacement schedules by identifiying the battery
health from which operational problems start to become more frequent.
* Smart grouping : Detecting statistical commonalities of devices with performance issues in large
mobile device deployments.
Awards
* Lead researcher for £1m Innovate UK supported R&D project on machine learning in mobile
device analytics (2019-2023).
* B2M's data anayltics product was recognised by Gartner as a leading provider in mobile and
wireless analytics (2017).
Patents
* Locating devices , GB2581862/US11516764B2, 2021
* Battery Stock Management , GB2604613, 2023
Tech stack
Python (Pandas, Numpy, Scikit, Matplotlib, XGBoost, PyMC3) for data analysis, prototyping and
machine learning tasks. Javascript/NodeJS for scripting and server-side production system.
AWS /Amazon web services (DynamoDB, EC2, ElasticSearch, S3, Lambda, Kinesis, SQS) for hosting.
Java/Android for mobile device client application.
As part of an agile development team, he worked on a cloud-based big-data software-as-a-service
product. He had the lead responsibility for data analytics, machine learning and statistical methods .
Projects (selected)
* Drop detection : Detecting drops of mobile devices with random forest/gradient boosted decision
trees trained on device accelerometer data.
* Anomaly detection : Learning the variability of device performance measures for a population of
mobile devices using a Bayesian statistical model to detect changes in device performance after
an application update.
* WiFi location : Learning locations of Wifi access points based on their interaction with mobile
devices and using the learnt location to warn about misplaced mobile devices.
* Battery health thresholds : Optimising battery replacement schedules by identifiying the battery
health from which operational problems start to become more frequent.
* Smart grouping : Detecting statistical commonalities of devices with performance issues in large
mobile device deployments.
Awards
* Lead researcher for £1m Innovate UK supported R&D project on machine learning in mobile
device analytics (2019-2023).
* B2M's data anayltics product was recognised by Gartner as a leading provider in mobile and
wireless analytics (2017).
Patents
* Locating devices , GB2581862/US11516764B2, 2021
* Battery Stock Management , GB2604613, 2023
Tech stack
Python (Pandas, Numpy, Scikit, Matplotlib, XGBoost, PyMC3) for data analysis, prototyping and
machine learning tasks. Javascript/NodeJS for scripting and server-side production system.
AWS /Amazon web services (DynamoDB, EC2, ElasticSearch, S3, Lambda, Kinesis, SQS) for hosting.
Java/Android for mobile device client application.
Flight test planning
Designed and built a machine learning model (genetic algorithm) to optimise flight test planning for the
commercial aviation industry.
Designed and built a machine learning model (genetic algorithm) to optimise flight test planning for the
commercial aviation industry.
Teaching tutorials in theoretical computer science, artificial intelligence, and logic.