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Lead Software Developer / Big Data Engineer Resume

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Ann Arbor, MI

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TECHNICAL SKILLS:

Languages: C, C++, Java, JavaScript, Python, PHP, Ruby on Rails, Scala, R, C#, Go, HTML/CSS

Frameworks/APIs: .NET, Amazon Web Services, AlchemyAPI, Hadoop, Apache Spark, MongoDB, SQL databases

Techniques: Agile development, Object Oriented Programming ( OOP ), Distributed Systems, Cloud Infrastructure, Remote / Autonomous Development, iOS Development, Open - Source Software, Large-Scale Software Development

PROFESSIONAL EXPERIENCE:

Lead Software Developer / Big Data Engineer

Confidential, Ann Arbor, MI

  • Developed web-based apps leveraging machine learning and ­­­­A­NN techniques for research in healthcare and socioeconomics
  • Implemented solutions using Angular, Go, Scala, and jQuery+javascript
  • Increased development efficiency by directing and coordinating multiple development teams in several timezones
  • Created and developed custom versions of popular ML frameworks, including Spark and Hadoop

Lead Software Engineer

Confidential, Ann Arbor, MI

  • Developed software allowing users to check backgrounds of medical practitioners
  • Employed machine learning algorithms to recommend physicians for patients
  • Implemented solutions using Java, C#, and Javascript, on top of a MongoDB non-relational database
  • Increased project turnaround by 50% by employing Agile development methodologies (TDD + DevOps)
  • Implemented solutions using R, Java and C#, within the Hadoop framework

Data Science Researcher

Confidential, Ann Arbor, MI

  • Developed machine learning based image-recognition software for identification and classification of cells
  • Decreased procedure time by 35% by introducing improved automation techniques
  • Implemented results using R, with a JavaScript-based front-end
  • Oversaw transition towards the Hadoop framework

Machine Learning Analyst

Confidential. Kalamazoo, MI

  • Developed software for identification and classification of potential anticancer drugs
  • Employed supervised and reinforcement learning techniques to provide viability rankings of proposed treatments
  • Tested and evaluated several classification algorithms (Naïve Bayes, logistic regression + Adaboost, Linear Weighted Regressions, etc.)
  • Implemented solutions in R, Scala, and Java

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