**Any opinions expressed on LinkedIn are my own and do not represent Amazon or any of my previous employers.** Meticulous and innovative, with ~9 years of relevant professional experience, research and educational background to contribute towards impactful corporate objectives in a Data Engineering role. AREAS OF EXPERTISE ==================== • Predictive Analytics: Proven success identifying relationships among historical and transactional data to glean insights. Hands-on experience with visualization tools used in model deployment. Track record of aligning meaningful statistics with decision support systems. • Software Development: Experience with object-oriented languages like Python and Java. Sound knowledge of design patterns. In-depth understanding of SOLID principles. • Machine Learning/Natural Language Processing: Well-versed with various techniques used in automation of analytical models. Proactive in devising creative strategies to provide complex analyses of Big Data repositories. • Data Mining/Databases: Excel at identifying trends within large data sets. Able to leverage enterprise tools to deliver accurate models. Talent for adapting solutions within highly-regulated industries. Adept with database administration and SQL query tuning. TECHNICAL PROFICIENCY ===================== • Languages: Python, Java, Scala, Ruby, SQL, TypeScript ASP.NET 4.0, PHP, D3.js • Software: R, Tableau, QlikView, SAS, MS Office, Git, AWS • DB: Oracle, Redshift, MySQL, SQL Server, PostgreSQL, MongoDB • OS: AIX, Linux, Solaris, HP-UX, Windows, Mac OS X • Big Data Technologies: Spark, Hadoop, EMR, MapReduce Techniques: Predictive & Statistical Modeling, Parts of Speech Tagging, Lemmatization, TF-IDF Vectorization, Sampling, Supervised & Unsupervised Machine Learning, Data Pipelining, Extract – Transform – Load, Object Oriented Programming.