Data Science/Machine Learning Director

Bachelor’s degree with concentration in Computer Science, Engineering or another quantitative field
10+ years of applicable professional experience
Data-oriented personality
Ability to synthesize the requests received from team members at client sites
Desire to actively engage in geographically dispersed teams
Capability to be a creative, innovative problem solver – but using simple ideas
Knowledge of any of the following languages: Python, JavaScript, C#, or similar plus familiarity with at least one ETL tool (such as Alteryx, KNIME, SSIS, Pentaho, or DataStage)
Motivated to discover and learn new analytical techniques and software tools to improve the quality of our work
Strong verbal and written communication skills in English. Proficiency in other languages is a plus
Ability and willingness to work long hours and travel if necessary, to meet client demands
Ability to work full-time in an office and remote environment; physically able to sit/stand at a computer and work in front of a computer screen for significant portions of the workday
Willingness to work outside of normal U.S. business hours, and in particular as unique projects/needs arise

Create ETL workflows, scripts, statistical models, and visualizations
Take responsibility for the design, build, test, execution, and support of the data migration, cleansing, wrangling, etc.
Select features, build and optimize classifiers using machine learning techniques
Execute machine learning projects using state-of-the-art methods
Extend company’s data with third party sources of information when needed
Create automated anomaly detection systems and constant tracking of its performance
Collect data from a wide variety of corporate databases
Parse data out of poorly structured XML and invalid HTML documents
Use regular expressions to extract information from un-structured text documents
Deal with missing data through multiple-imputation or the use of advanced models
Automate boring tasks with scripts
Build effective, reliable, and robust ETL processes that govern the data ingestion flow
Design database models, consistent table structures, and advanced dimensional schemas that carry out data quality and consistency standards
Apply modeling approaches, business intelligence patterns, and data management techniques
Demonstrate advanced SQL skills, such as CTEs and window functions
Review and analyze legacy code/scripts to understand data processing logic and business rules
Ability to apply statistical learning languages to build predictive models
Use interactive data visualization tools, such as Tableau and Power BI to present results

Experience with common data science toolkits, such as Python, PySpark, R. Excellence in at least one of these is highly desirable
Understanding of cloud architectures. Some knowledge in Azure, AWS or GCP is desired
Distributed systems knowledge, especially of HDFS and the Hadoop ecosystem