Principal Data Scientist, Predictive Modeling

Responsibilities:

• Design, test and implement recommendation engine models to turn sparse datasets into synthetic datasets.  This includes writing code from scratch.  Must be proficient with reading, manipulating and analyzing big data and writing new code to build models.
• Prove model validity through analytic research (e.g. holdouts) and speak to predictive power internally and externally to clients as needed.
• Use iterative modeling to determine the ideal parameters for the sparse data - e.g. minimum level of completeness, key required datapoints that drive higher predictive accuracy, minimum number of datapoints required for a given predicted variable, etc.
• Using the analytic findings from ideal parameter exploration, consult with internal and external partners on the best way to source the ideal sparse data.
• Support screening and hiring of other data scientists in the mid to long term future.  Support onboarding and development of more junior data scientist staff.  Serve as a subject matter expert for others internally and externally.  Provide technical assistance in predictive modeling methodologies, data manipulation, fusion, and modeling.
• Define and implement a vision for scaling new models across increasingly larger datasets - driving for efficient speed, computer usage, etc.
• Support automation for scaling routinized processes.
• Support pilot programs for R&D purposes.  
• Conduct tactical or strategic analyses to address business and customer opportunities.
• Utilize tools such as Python, R, SPSS, etc. to perform complex data analysis, develop tools for automating procedures.
• Develop, test, and implement high quality, modular python code that can be seamlessly integrated into an existing production system.
• Develop and implement machine learning solutions to leverage big data from internal and external sources.
• Assist with ad hoc analyses and projects.

Qualifications:

• Strong Educational Background: Degree in a quantitative field like Math, Statistics, Computer Science, or Economics.
• Experienced Data Scientist: 10+ years of experience, with a focus on predictive modeling and recommendation engines.
• Technical Proficiency: Skilled in Python (including pytorch), statistical tests, and machine learning techniques (Decision Trees, Random Forests, Neural Networks, etc.).
• Data Management and Visualization: Familiarity with SQL, relational databases, and BI tools like Tableau and Spotfire.
• Excellent Communication and Collaboration: Strong critical thinking, problem-solving, and communication skills, with experience in a fast-paced environment.