Resume
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Peter Jensen is an experienced data science professional based in Melbourne, Australia with a Master's degree in astrophysics. He has worked on predictive analytics and machine learning projects for several Australian companies. Currently seeking new opportunities, he has skills in areas such as statistical analysis, machine learning algorithms, and programming languages like R and Python.
Resume
- 1. Page 1 of 3 Peter Jensen Data Science Professional [email protected] | +61 401 698 268 https://www.linkedin.com/in/peterjensenau Summary Experienced data science professional based in Melbourne, Australia with interests in predictive analytics, machine learning, and applying mathematical models from physics and econometrics to solve interesting and novel business problems. I earned my Master of Science (Research) degree at the Centre for Astrophysics and Supercomputing at Swinburne University where I developed skills in mathematical and statistical analysis, computer programming, algorithm design, and working with big data sets in a supercomputing environment while investigating the role of galaxy cluster mergers on galaxy evolution. Since completing my masters, I have worked as an agency-based data science consultant on projects for Australian Gas Networks, Bunnings, Mazda, RMIT, Telstra, and the Water Board of Western Australia. I am also a member of the Institute of Analytics Professionals of Australia (IAPA). Currently, I am working on a short-term project as a data science contractor but I am open to new roles from May 2017. Experience Senior Data Scientist CHE Proximity, Melbourne, Australia Mar 2016 – Feb 2017 Predictive analytics using machine learning algorithms such as neural networks, SVM, random forests, k-means clustering, OLS minimisation, linear regression, and logistic regression Applying machine learning algorithms to build marketing mix, marketing attribution, propensity, and template-fitting models in R and Python Data science lead pitching ideas and presenting insights and results to Australian Gas Networks, Mazda, RMIT, and Telstra Analytics and reporting dashboard design and implementation using Tableau, R, and SQL Server Database design and implementation in SQL Server Website optimisation and reporting using Google Analytics and Adobe Analytics Geographical information systems using Google Maps API and Tableau Team leadership and mentorship of junior data scientists and interns Data Scientist The Brand Agency, Melbourne, Australia Jan 2015 – Mar 2016 Developing marketing mix, media optimization, and customer segmentation models for Bunnings and Water Board of Western Australia Natural language processing and fuzzy matching algorithms Data pipeline development and database administration Digital marketing tracking and analytics Traditional media tracking and analytics Data insights reporting and data strategy Dashboard development Mathematics Lecturer Swinburne University of Technology, Melbourne, Australia Mar 2009 – Nov 2013 Teaching calculus and linear algebra tolarge classes of engineering students in an engaging manner Grading assignments and exams and providing feedback to students Mentoring and providing additional help to interested students
- 2. Peter Jensen|Data Science Professional | [email protected] | +61 401 698 268 | https://w w .linkedin.com/in/peterjensenau_ Page 2 of 3 Education Master of Science (Research) in Astrophysics Swinburne University of Technology, Mar 2009 – Feb 2014 Thesis: “Dynamically-Driven Galaxy Evolution in Clusters of Galaxies” Bachelor of Science (Honours) in Mathematics and Physics The University of Queensland, Mar 2004 – Jun 2008 Thesis: “The Colours of Galaxies in Intermediate X-ray Luminosity Galaxy Clusters” Bachelor of Business (Management) / Bachelor of Arts in Computer Science, Marketing, and Spanish The University of Queensland, Mar 1998 – Nov 2002 Certifications Adobe Certified Expert: Analytics Business Practitioner (9AO-381) Pearson VUE, 2017 Coursera Data Science Specialisation The Data Scientist’s Toolbox, Coursera, Dec 2014 R Programming, Coursera, Feb 2015 Google Analytics Individual Qualification (IQ) Google, 2017 Skills Data Analysis Skills and Programming Languages A/B and Multivariate Testing Adobe Analytics (Omniture) Analytics Dashboards and Reporting Big Data C CRM Systems Data Mining Experiment Design Fuzzy Matching Geographical Information Systems (GIS) Google Analytics Fortran Image Processing and Analysis Java Machine Learning Mathematica Mathematical/Statistical Modelling Mathematics MATLAB Microsoft Access Microsoft Excel Microsoft SQL Server mySQL Natural Language Processing (NLP) Predictive Analytics Python R REST APIs Shell Scripting SPSS SQL Statistics Tableau Web Analytics Other Skills Adobe Audience Manager Agile Methodologies Astrophysics Digital Marketing Foreign Languages (French, Spanish, Italian) LaTeX Linux / Unix Microsoft PowerPoint Microsoft Word Photoshop Physics Public Speaking Published Author Stakeholder Management University Teaching Website Optimisation
- 3. Peter Jensen|Data Science Professional | [email protected] | +61 401 698 268 | https://w w .linkedin.com/in/peterjensenau_ Page 3 of 3 Projects Australian Gas Networks Appliance Mix Project Built a model to predict the mix of gas appliances installed in households using consumption data. The model achieved a classification success rate of over 80%. Classifications were used in conjunction with geographic and demographic data to build a new customer segmentation model. Mazda Repurchase Project Built a propensity model to identify customers likely to repurchase a vehicle and the vehicle likely to be purchased. The marketing team tailored eDMs based on the output of the model. Preliminary A/B testing indicated that repurchase rates were boosted by approximately 3-4% relative to the control group. Bunnings Media Optimisation Project Built a prototype marketing mix model tool to predict and optimise daily sales volumes based on proposed and in-market advertising. The model achieved R-squared values in the range of 0.75-0.85 and MCMC modelling suggested 5-10% sales uplifts were possible without changing overall media spend. Honours & Awards Australian Postgraduate Award (APA) Scholarship Commonwealth Government of Australia, awarded 2009 – 2012 Bok Medal Prize Awarded for the most outstanding astrophysics honours thesis submitted to an Australian university Astronomical Society of Australia, Jul 2009 Dean's Commendation for High Achievement The University of Queensland, awarded every semester 2004 – 2008 Overall Position (OP) 1 Equivalent to ATAR score ≧ 98.95 Queensland Studies Authority, Dec 1997 Publications “Dynamically-Driven Galaxy Evolution in Clusters of Galaxies” Jensen, P. C., 2014, Australasian Digital Theses Collection, oai:vtl.cc.swin.edu.au:swin:42164 “Red sequence modal colour gradients across intermediate X-ray luminosity galaxy clusters” Jensen, P. C., & Pimbblet, K. A., 2012, Monthly Notices of the Royal Astronomical Society, 422, 2841 “The role of stellar mass and environment for cluster blue fraction, AGN fraction and star formation indicators from a targeted analysis of Abell 1691” Pimbblet, K. A., & Jensen, P. C., 2012, Monthly Notices of the Royal Astronomical Society, 426, 1632 “Bistable Switching between Low and High Absorbance States in Oleate-Capped PbS Quantum Dots” Fernée, M. J., Jensen, P. C., & Rubinsztein-Dunlop, H., 2009, ACS Nano, 3, 2731 “Unconventional photoluminescence upconversion from PbS quantum dots” Fernée, M. J., Jensen, P. C., & Rubinsztein-Dunlop, H., 2007, Applied Physics Letters, 91, 043112 “Origin of the Large Homogeneous Line Widths Obtained from Strongly Quantum Confined PbS Nanocrystals at Room Temperature” Fernée, M. J., Jensen, P. C., & Rubinsztein-Dunlop, H., 2007, Journal of Physical Chemistry C, 111, 4984 “Highly efficient luminescence from a hybrid state found in strongly quantum confined PbS nanocrystals” Fernée, M. J., Thomsen, E., Jensen, P. C., & Rubinsztein-Dunlop, H., 2006, Nanotechnology, 17, 956