Data Scientist • Senior

Who you are

You have 5+ years of experience in data science, particularly in advanced risk modeling and machine learning. Your expertise includes designing and validating complex models that solve real-world business problems, especially in the healthcare domain. You are proficient in Python and SQL, and you have a strong understanding of machine learning methodologies, including deep learning and natural language processing. You thrive in collaborative environments and enjoy working with large datasets to extract meaningful insights. You are detail-oriented and have a knack for feature engineering, ensuring that your models are robust and reliable.

Desirable

Experience with healthcare datasets is a plus, as is familiarity with modern ML platforms. You are comfortable working in a hybrid environment and can adapt to the needs of your team while maintaining a focus on delivering high-quality results.

What you'll do

In this role, you will conduct in-depth data analysis and advanced feature engineering using large, complex healthcare datasets. You will design, develop, and validate advanced machine learning and AI models, including regression/classification, deep learning, and NLP. Your work will directly impact Oscar's risk adjustment strategies, helping to improve the overall member experience. You will collaborate closely with other data scientists and stakeholders to ensure that your models align with business objectives and provide actionable insights. You will also be responsible for deploying your models into production and monitoring their performance over time.

What we offer

Oscar provides a competitive salary range of $158,400 to $207,900 per year, along with comprehensive employee benefits, including participation in an unlimited vacation program and company equity grants. You will have the opportunity to work in a supportive environment that values innovation and collaboration. The hybrid work schedule allows for flexibility, with three days in the Los Angeles office each week, fostering team cohesion while also accommodating your personal work preferences.