The Principal Data Scientist role is primarily responsible for developing, interpreting and implementing statistical models and creating other analytical solutions using the latest statistical and data science techniques for business problems across the organization.
- Develop and maintain predictive models investigate data quality
- Explore feature selection and model tuning for improved performance
- Collect performance data and monitor model assessment
- Drive data understanding to ensure the alignment of data science to business goals
- Work with Product Team to align on data meaning and industry practices and terminology
- Work with Data Platform Team to ensure concept and semantic integrity across data layers
- Work with Clinical and Growth teams to translate business problems into solutions and be able to effectively communicate about the data issues to the business.
- Able to research any other data points that the team can utilize for predictions or reporting
- Manage Data Governance Framework Research and analyze the effectiveness of products
- Use techniques such as PSM to estimate, understand, and monitor product performance
- Research and implement new machine learning methods to improve risk management products
- Support BI and analytics across departments
- Provide consultative expertise as needed
- Work to provide self-serve reports, dashboards, and information on predictive models and customer performance
- Advance machine learning strategy and road map planning working across the modeling community
- Work closely with the heads of Clinical Transformation, Client Experience, Technology, Operations, Product and the Business lines to identify new or enhanced capabilities that will advance strategic objectives
- Build and lead a high-performing team of data scientists, helping to enhance their skills, while creating a fun and engaging culture that helps them prosper.
- Foster career growth and provide thoughtful mentorship to develop employees at varying levels of experience.
- Ensure our production level models are performant and meet industry standard and best practices
- Champion Data Understanding and a culture of Data Science throughout the company
- Inform data analysis best practices across company teams that are statistically rigorous, and provide an interpretation of findings that a reliable and are defensible to the industry
- 8+ years of professional experience working as a Senior Data Scientist in machine learning,
- 3 or more years managing and mentoring a team of data scientists in a production environment.
- Demonstrated experience in data governance and data quality best practices.
- Experience in bringing a data science product to market working across business functions
- Broad knowledge of supervised machine learning approaches and performance. Boosting, random forests, linear regression, logistic regression, artificial neural networks, convolutional neural networks, naive bayes, etc.
- Moderate knowledge of traditional statistics. Confidence intervals, hypothesis tests for independent samples vs related samples, p values, type I vs type II error.
- Expert ability to communicate findings and solutions clearly to a variety of audiences, as well as draft clear, comprehensive specifications for engineers or explaining analytics concepts to non-experts.
- Support Data Science Team in at least one area.
- Statistical hypothesis tests: Advanced degree in Statistics. Ability to translate analysis question into a testable hypothesis and run a well-designed experiment.
- Training / mentoring: Professional experience mentoring new team members and creating training material.
- Undergraduate degree from an accredited college/university in Computer Science, Statistics, Mathematics, Engineering, Bioinformatics, Physics, or related fields with a strong mathematical background, with ability to understand data mining algorithms and machine learning.
- Strong proficiency in R, python, or SAS and demonstrated experience with programming languages.
- Must have current skills with R or have used it in the past.
NICE TO HAVE'S:
- Graduate degree from an accredited college/university in Computer Science, Statistics, Mathematics, Engineering, Bioinformatics, Physics, or related fields with a strong mathematical background, with ability to understand data mining algorithms and machine learning.
- Healthcare experience is a plus.
- Public GitHub containing example work.
- Knowledge of unsupervised machine learning approaches. Principal component analysis, kernel principal component analysis, clustering etc.
This is a remote position. Candidates can work from anywhere in the US.