An employer is seeking a Data Scientist to join a large automotive company sitting fully remote. This person will be interfacing with various teams across the enterprise such as marketing, pricing, and operations. They will be responsible for understanding the problems presented to them and turning them into something that will help the business in the end. The ideal candidate will be able to collaborate and communicate well with business partners. Other responsibilities include:
Lead the development of Driven Customer Total Lifetime Value (LTV), including analyzing and identifying opportunities to increase lifetime value across full portfolio of brands.
Develop pricing elasticity models for all brands and present the results to executives and brand leaders.
Develop and deploy spend optimization models across brands
Lead ML projects from understanding and defining the business need through deploying completed models to the cloud with model monitoring to deliver business impact to the organization.
Mentor and guide data scientist and analysts in statistics and machine learning
Design and leverage various types of customer segmentation models and life cycle analytics to enhance targeting efforts and drive measurable results
Design and execute customer targeting models that drive incremental response for the business (CRM development) as related to acquisition, retention, and customer recovery
Apply design of experiments (DOE) techniques for A/B and multivariate testing to drive data driven business decisions
Working closely with key business partners to create and execute a learning plan that drives actionable consumer insights
Masters Degree in Computer Science, Statistics, Mathematics, or relevant field
Strong statistics background with experience applying in a business environment
Expertise in Retail or Logistic Analytics and Data Science
Experience with cloud-based (AWS, Google Cloud, etc.) tools for developing, deploying, and monitoring machine learning models and pipelines
2-4 years of experience in a data scientist, or similar, role building and deploying ML models.
Familiarity with various data modeling techniques (linear/logistic regression, ANOVA, ARIMA, Clustering, time series, machine learning)
Strong command of advanced data analysis tools (R, Python, etc.), and querying languages (SQL)
Articulate with excellent verbal and written communication skills
Highly effective at cultivating relationships across diverse teams (technical and business teams)
Highly organized & able to manage analytic projects end-to-end with a high degree of communication and commitment to deadlines
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