Data Scientist

United States

Full-time

Remote

$92K/yr - $116K/yr

Entry, Mid Level

eHealth is a company dedicated to guiding consumers through their health insurance options. They are seeking a Data Scientist to develop innovative machine learning solutions that enhance decision-making and improve customer experience.

Apply Now

Responsibilities

Work under the mentorship of senior data scientists to tackle high-impact projects and solve complex problems.
Develop cutting-edge machine learning models and continuously improve them to address new challenges and expand what can be achieved through data science.
Perform open-ended data exploration to identify key insights and solve complex problems, such as recommendations, call routing optimization, and more.
Engage in all stages of the project lifecycle, from gathering requirements and preparing data to generating hypotheses, coding, testing, and deploying data science products.
Design and implement both online and offline experiments to validate hypotheses and evaluate model performance.
Collaborate with business partners and cross-functional teams to identify opportunities for innovation and leverage data to improve business outcomes.
Work closely with engineers to deploy and maintain machine learning models in production.
Contribute to fostering a culture of data-driven decision-making by providing actionable insights that support business needs.
Demonstrate eHealth’s values in your behaviors, practices, and decisions.

Qualification

Required

Bachelor’s degree in Computer Science, Mathematics, Statistics, Data Science, Physics, or a related technical field.
2+ years of professional experience in data science, including building predictive or descriptive models and creating metrics to evaluate them.
Proficiency in Python and scripting languages (e.g., Unix shell), with solid experience in using version control tools like Git.
Experience with A/B testing, multivariate experimentation, ranking systems, and recommendation algorithms.
Familiarity with machine learning frameworks such as Scikit-learn, TensorFlow, PyTorch, and Spark ML.
Strong knowledge of SQL, with experience in distributed systems (e.g., Snowflake, Spark) and NoSQL databases (e.g., MongoDB).
Proven ability to collaborate directly with cross-functional teams (product managers, business stakeholders) to gather requirements and present machine learning solutions.
Excellent communication and interpersonal skills, with the ability to explain technical concepts to non-technical audiences.