Lead Data Scientist

Integriant Ventures Insurance Services, Inc. (dba Mayberry Advisors Insurance Services) has multiple openings for the following position in McLean, Virginia. To apply, upload resume and cover letter to https://integriant.isolvedhire.com/jobs/1209958-424209.html and reference job title. EOE. Principals only.

Lead Data Scientist (Wage Offer: $165,000/yr): Develop Data Scientist and Machine Learning Strategy to improve business performance. Leverage statistical analysis, forecasting methods, product development, data visualization, and storytelling to increase operational resiliency. Partner with stakeholders across Marketing, Sales, Operations, Engineering, Growth, and Finance to derive requirements, scope data services and systems, and drive results to improve insurance customer experience. Lead end-to-end process including conception, development, production, deployment, and monitoring of data and machine learning models and services. Lead the team’s work on pricing, recommendation, lead scoring, and customer retention. Provide technical leadership to other scientists and engineers working on data analytics projects and deliverables aiming at increasing customer LTV, targeted marketing, retention, and profitability. Manage data flows between all SaaS products and the data lake. Manage Data Pipelines to pipe operational data into AWS S3 Data Lake. Work with the Company’s data analytics team to produce reliable reporting. Work on developing ML models for Customer LTV, Churn, and profitability. Work with marketing to develop models/algorithms for targeting optimal customer.

Telecommuting within 30 miles of McLean, VA.

REQUIREMENTS: Bachelor’s degree or foreign equivalent in Computer Science, Engineering, Mathematics, or related field. 5 years of progressive, post-baccalaureate experience as a Data Scientist, or a related occupation.

SKILLS: Must have:

1. Experience applying machine learning to real-world customer behavior data problems (targeting, pricing, recommendation, personalization) and deploying and scaling models into a production environment.

2. Experience with algorithms, statistics, and predictive modeling.

3. Experience with Machine Learning (Supervised, Unsupervised, Deep Learning, Bagging, Boosting techniques).

4. Experience with Python and experience with Machine Learning libraries and platforms.

5. Experience with AWS Cloud Technologies for Machine Learning/Artificial Intelligence.

6. Experience with telecom data and working with call-based data sets.