Data Scientist / AI/ML Engineer

Required Skills

Requirements

• 6+ years as a Data Scientist. Degree'ed professionals are preferred (4 additional years of experience could replace degree)

• Python and SQL are required. Advanced proficiency in Python and Spark/Scala for classical statistical analysis and data modeling, machine learning and ETL processes. Intermediate to advanced ability to create data visualizations using Python.

• AWS Cloud knowledge (AWS migration considered big plus). Must have solid knowledge in one or more AWS Service (SageMaker is REQUIRED, open to AWS Glue, Athena, Lambda etc..)

• Ability to write production-ready code including documentation and unit tests.

• Experience with machine learning methods like k-nearest neighbors, random forests, ensemble methods and more = Required

• Knowledge in Artificial Intelligence (AI) = Required

• Proficiency in data science modeling - Machine Learning, Deep Learning, Decision Trees, Random Forest, Neural Networks, Supervised/Unsupervised Learning, Forecasting, Predictive Modeling and Clustering.

• Strong background in machine learning using unsupervised and supervised methods.

• Deep knowledge of fundamentals of machine learning, data mining and statistical predictive modeling, and extensive experience applying these methods to real world problems

• Fluency in SQL and other programming languages. Some development experience in at least one scripting language (PHP, Python, Perl, etc.)

• Strong skills in software prototyping and engineering with expertise in applicable programming and analytics languages (Python, R, Spark/Scala) and various open source machine learning and analytics packages to generate deliverable modules and prototype demonstrations of their work.

• Proven experience of using Python Machine Learning & Data Pre-processing Libraries. (Scikit Learn, Numpy, Pandas)

• Ability to initiate and drive projects to completion with minimal guidance

• The ability to communicate the results of analyses in a clear and effective manner

• Ability to transform concepts into practical solutions. Experience with data science/ML techniques

• Machine Learning experience -- Successful implementation of ML solutions

• Communication Ability -- Experience/comfort in presenting abstract concepts

• Preferred, not required:

• Preferred experience with a statistical package such as R, MATLAB, SPSS, SAS, Stata, etc.

• Proficiency with healthcare analytics and data structures is preferred.

• Desired interdisciplinary skills include big data technologies, ETL, statistics and causal inference, Deep Learning, modeling and simulation. - Experience with large data sets and distributed computing (Hive/Hadoop) a plus.

• Leading data science projects or teams (as the most technically advanced team member) or working independently on data science projects.