Motivated data scientist with a Ph.D. in computational astrophysics and machine learning certificate from Stanford University. Six years of experience in algorithmic solutions, programming, and predictive analysis. Proficient in big data technologies with a strong math and statistics background. Passionate about explaining data science to non-technical business audiences. Works well under pressure both individually and as part of a team. Seeking an opportunity to drive business effectiveness through making recommendations based on data findings.

• Machine Learning and Statistical Methods: Supervised and unsupervised learning, linear regression, logistic regression, neural network, decision boundary, decision trees, support vector machines, k-nearest neighbour, random forest, naive bayes, clustering (K-means), dimensionality reduction, anomaly detection, recommender systems, online learning, map-reduce, data-parallelism, hypothesis testing and confidence intervals, time series analysis, predictive and behaviour modelling, forward and back-propagation, extrapolation, Monte-Carlo method, principal component analysis, data cleansing and distribution, and stochastic differential equations.

• Software and Programming Languages: Advanced programming skills with six years of experience in Python (pandas, scikit-learn, matplotlib, seaborn, NumPy, SciPy, gensim), R, C, C++, Matlab, and Shell scripting. Proficient in parallel programming, high performance computing (frequent Compute-Canada user), and three years of hands-on experience using Hadoop big data processing and clustering ecosystem including HDFS, HBase, Spark, Hive, Pig, Impala, Hue, Oozie, Flume, Sqoop, Mahout, and Zookeeper. Expert in visual analytic tools with two years of experience using Tableau and Paraview. Knowledgeable in database management systems with three years of experience in SQL (MySQL, PostgreSQL, T-SQL, SQLite). Skilled user of different operating systems such as Linux, macOS, and Windows. Daily user of LaTex, Microsoft Excel, and PowerPoint.

• Selected Coursework: Machine Learning (Stanford University), Hadoop Platform (University of California at San Diego), Algorithms and Programming (5 courses), Numerical Analysis, Visual Analytics, Linear Algebra, Probability and Statistics, Random Matrix Theory, Stochastic Processes, and Stochastic Simulation.

• Advanced Machine Learning - Stanford University - 2017

• Hadoop Platform and Application Framework - University of California at San Diego - 2017

• Data Science in Python Pandas - University of Michigan - 2017

• Applied Plotting, Charting & Data Representation in Python - University of Michigan - 2017
  

• Applied Machine Learning in Python - University of Michigan - 2017

• Google Cloud Platform Big Data and Machine Learning Fundamentals - Google Cloud - 2017

• SQL: Introduction, Table transformation, and Analyzing Business Metrics - CodeCademy - 2017  

• Heusen, M., Shalchi, A., "Numerical test of analytical theories for perpendicular diffusion in small Kubo number turbulence", The Astrophysical Journal, April 2017 
• Heusen, M., Shalchi, A., "Simulations of energetic particles interacting with nonlinear anisotropic dynamical turbulence", Astrophysics and Space Science Journal, August 2016
• Heusen, M., Shalchi, A., "Detailed numerical investigation of the Bohm limit in cosmic ray diffusion theory", The Astrophysical Journal, January 2014 

• "Investigating cosmic ray transport using supercomputers", International Cosmic Ray Conference, The Hague, Netherlands, August 2015 
• "Big data for solar physics", The Big data workshop, University of Manitoba, Winnipeg, Canada, Feb. 2015
• "Solar storms mining using numerical simulations", The American Geophysical Union Meeting, San Francisco, USA, December 2014

Local Organizing Committee, Canadian Astronomical Society Meeting, Winnipeg, May 2016

• Overwhelming experience spanned over eight months of preparations.
• Boosted my networking, fundraising, team working, decision making, prioritizing, and organizational skills.

Graduate Student Organizing Committee, UK National Astronomy Meeting, Manchester, July 2012

• Duties include: chairing scientific sessions, preparing material for conference delegates, and guiding and information.

• Organizer and regular speaker of the monthly open houses at the Ewen observatory and the Lockhart planetarium hosted jointly by the Department of Physics and Astronomy at the University of Manitoba and Royal Astronomical Society of Canada (September 2014 - January 2017)
• Invited guest speaker at the monthly meeting of the Royal Astronomical Society of Canada - Winnipeg Chapter (February 2015)
• Organizer and demonstrator at the annual Science Rendezvous event - the largest science festival in Canada - running dozens of demonstrations and operating sessions to introduce physics and astronomy to the general public (every second week of May since 2013)
• Co-host and guide at the Jodrell Bank Centre for Astrophysics public engagement series performing regular experiments and setting up telescopes for the public at the museum of science and industry in Manchester city, UK (September 2011 - September 2012)

• Permanent resident of Canada 
• Date of Birth: 14/03/1991

• Social Studies,- Grade 6 through 8

• Student Motivation, Classroom Management
• Team Teaching Approach, Creative Lesson Plan Developement
• Curriculum Development, Positive Atmosphere Promotion 
• Technology Integration 

• Facilitated child-centered learning through creative activities and independent exploration
• Created and inviting and stimulating environment through thoughtful interactive classroom set-up
• Assisted children with homework

• Communicated with parents on a daily basis of child's progress and activities participated in for the day

• Developed lesson plans encouraging creativity, social skills and critical thinking skills
• Executed classroom management through positive reinforcement and keeping students on task
• Tutored students on various subjects