The Automotive Data Science team at Epsilon supports OEMs and dealerships by developing advanced analytics and machine learning solutions to drive customer acquisition, engagement, and retention. The Data Scientist II role involves applying AI and ML techniques, designing MLOps pipelines, deploying and monitoring models, improving methodologies, and translating insights into business recommendations. Requires 2-4 years experience, proficiency in Python, PySpark, SQL, ML frameworks, and knowledge of MLOps, Databricks, AWS or Azure. Exposure to automotive industry and digital marketing preferred. Strong analytical, communication, and collaboration skills needed.
What you'll do
Apply AI, machine learning, and deep learning techniques including regression, classification, clustering, tree-based algorithms, support vector machines, neural networks, recommender systems, and marketing attribution models to solve complex business problems
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Collaborate with cross-functional teams to deliver production-ready solutions
Requirements
Bachelor’s or Master’s Degree in Engineering or a quantitative field (Statistics, Economics, Mathematics, Data Science, or Business Analytics)
2–4 years of relevant experience as a Data Scientist or Machine Learning Engineer
2+ years of hands-on programming skills using Python, PySpark, SQL, and ML frameworks
Good understanding of the end-to-end machine learning lifecycle including data preparation, model development, validation, deployment, and monitoring
Good working knowledge of MLOps practices, Databricks, and Cloud Platforms AWS or Azure
Experience working with large datasets and developing scalable, efficient data science and machine learning solutions
Exposure to automotive industry and digital marketing campaigns preferred
Ability to work independently and navigate ambiguity
Analytical thinking and problem-solving abilities with a data-driven approach
Excellent written and verbal communication skills
Highly motivated, proactive, collaborative with good interpersonal skills
Opportunities for growth through learning, development and career advancementFocus on employee well-beingCollaborative work environmentFlexibility to balance work and personal lifeInclusive and diverse workplace
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