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SUMMARY:

+* years of data scientist, machine learning, and statistical modeling experience with the proof of track of developing and implementation of large-scale algorithms in an agile environment from the inception of an idea to launch resulting in delivering high corporate value. +5 years of experience applying R, Python, SAS, MATLAB, SQL, and big data tools such as Hadoop and Spark for algorithm development, data modeling, statistical learning, and data visualization. Inherently curious, energized by large challenges, and interested in making a significant impact on a globally scaled business. Professional in problem formulation and problem-solving skills and quickly learn, master new technology, and adapt easily to new concepts in pressured environments with capacity to perform under strict deadlines. Equally successful in both team and self-directed settings with strong communication skills. EDUCATION:

PhD. in Industrial Engineering, (Expert Systems & Knowledge Extraction), Expected May 2020 – Wayne State University, Detroit, MI

M.Sc. in Computer Science, (Machine Learning/Data Mining), December 2015 – Wayne State University, Detroit, MI

B.Sc. in Computer Hardware Engineering, December 2009 – University of Najafabad, Iran A.A.S in Electrical Engineering, May2006 – University of Applied Science and Technology, Iran TECHNOLOGY SUMMARY:

• Languages: C++, C#, Python, Scala, R, Java, SAS, MATLAB, ASP.NET, HTML, JavaScript, PySpark, SparkML, Sklearn, PyTorch

• Software: Alteryx, SAS Enterprise Miner, R.NET, Weka, RapidMiner, Knime, Orange, SPSS, Mathematica, Minitab, Knime

• Big Data & Databases: MySQL, Microsoft SQL Server, Oracle SQL, Hadoop, HIVE, SPARK, Neo4j, Stardog, AWS, SparkSQL, TensorFlow, SPARQL, Stardog

• Visualization tools: QlikView, Tableau, Taxonomy, FCM, Cognos, QuickSight

• Operating systems: Windows, Linux

• Microsoft: Visio, Word, PowerPoint, Excel, Visual Studio RESEARCH & WORK EXPERIENCE:

Data Engineer, Testing and Evaluation, Wayne State University, Detroit, MI, Jan 2019 – Dec 2019

• Worked on a complete data life cycle process including data gathering, cleansing, pre-processing, analyzing and managing the database.

• Analyzed data and generated the report to top university management team using IBM Cognos Business Intelligence.

Data Scientist Researcher, Wayne State University & Ford Motor Company, Detroit, MI, Oct 2017 –Oct 2018

• Developing smart integrated prediction and visualization platform for Resistance Spot Welding (RSW) in welded assembly at Ford Motor Company manufacturing

• Combining assembly design with welding process data to prediction

• Applying machine learning algorithms such as support vector machine, decision tree to predict the response parameter of RSW (i.e., nugget width) in order to confirm the quality of the weld and the assembly structure Data Scientist Developer, Wayne State University & John Dingell VA Medical Center, Detroit, MI, Oct 2016 – Oct 2017

• Designed and implemented dataflow from raw data to knowledge for VA.

• Implemented decision support system (Medical Evaluation and Decision Analytics (MEEDA)) using C#, R, R.NET, MVC, and Microsoft SQL Server.

• Performed batch and online modeling for medical device cost prediction.

• Developed a statistical modelling and visualization pipeline to predict and future cost for medical devices using C#, R, R.NET, MVC and Microsoft SQL Server. Applied ML tools to classify cost category. Sam Ameri

E-MAIL: adc44g@r.postjobfree.com phone: 248-***-****

Google Scholar

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Data Scientist Summer Intern, Credit Analytics – GDIA, Ford Motor Credit Company, Dearborn, MI, May 2016 – July 2016

• Cooperated with Global Scorecard team in to improve credit risk scoring model by performing machine learning and data analysis methods.

• Assessed the impact of rare events on the probability of payment (POP) and created a robust model using over/under-sampling.

• Developed SAS macros for data cleaning, validation, analysis and report generation. Data Scientist, Wayne State University, Detroit, MI, May 2014 – May 2016

• Defined and formulated student retention/attrition problem in an unprecedented method which impacted community.

• Utilized numerous statistical modelling and machine learning techniques, not only to predict who will drop out, but also when this is going to happen.

• Performed pre-processing, rescaling, data cleaning and applying exploratory data analysis using numerus tools.

• Analyzing different statistical methods and machine learning techniques in order to finding best approach to model student retention in Wayne State University such classification methods, survival analysis and time series techniques.

• Wayne State’s graduation rates have nearly doubled between 2011 and 2017, increasing from 26 to 47 percent according to the Integrated Postsecondary Education Data System (IPEDS).

• This work has been published and already cited by 43 researchers. Database Manager, Payeh Faragostar, Isfahan, May 2009 – Aug 2012

• Data cleaned & processed using Excel, Access and SQL.

• Designed and maintained database.