Data Scientist Lead

As our Lead Data Scientist, you will act as an internal business consultant to help optimize every facet of this organization. You will solve problems and answer questions – using data – for other departments to help us reduce costs, reduce errors, and be a better organization for our 17 million members.

What You'll Do
Every day, you will help solve business problems presented by stakeholders using data. You will partner with business teams to understand challenges, formulate analytical approaches, and deliver actionable insights that improve operational efficiency, customer experience, and business performance.
 

You will pull, curate, and analyze data from enterprise data platforms and cloud environments using SQL, Python, or similar tools. You will apply statistical methods, hypothesis testing, causal inference, machine learning, and advanced analytics techniques to identify trends, determine root causes, evaluate business strategies, and measure outcomes.

You will work with large-scale datasets and collaborate closely with data engineers to build scalable analytical solutions. Depending on the business need, your work may include exploratory analysis, dashboard development, predictive modeling, experimentation, causal impact studies, or machine learning solutions deployed into production environments.

You will also design, develop, and maintain machine learning and predictive models that support business decision-making and operational efficiency. As part of your day-to-day responsibilities, you will identify opportunities to automate manual processes, streamline analytical workflows, and develop scalable solutions that improve accuracy, consistency, and speed across the organization. This may include leveraging machine learning, generative AI, intelligent agents, and workflow automation technologies to create data-driven solutions that enhance business processes and user experiences.

You will partner with data engineers and business stakeholders to operationalize models, AI-powered applications, and automated processes, ensuring they are scalable, maintainable, and deliver measurable business value. Using tools such as Tableau, Power BI, or other visualization platforms, you will communicate findings and recommendations to technical and non-technical stakeholders.

You will translate complex analytical concepts into clear business insights and influence decision-making across the organization.
 

As a senior contributor, you will lead projects, mentor junior data scientists, and help guide cross-functional initiatives from concept through implementation.

Note: This is a remote role; however, the candidate must reside within a 100-mile radius of our Costa Mesa, CA office and be available to work on-site once a week on a regular basis, as well as 2–3 times per month for team meetings and collaboration days.

What You'll Need
To thrive in this role, you must have a strong foundation in statistics, data analysis, and business problem solving. You should be comfortable taking an ambiguous business problem, developing an analytical approach, and translating findings into actionable recommendations.
 

Required Qualifications

• Bachelor's degree in Statistics, Data Science, Computer Science, Mathematics, Engineering, Economics, or a related quantitative field.

• Minimum of 3 years of professional experience in data science, advanced analytics, business analytics, or a related field.

• Minimum of 1 year of experience leading projects, mentoring team members, or managing cross-functional initiatives.

• Advanced SQL skills with experience querying and analyzing large datasets.

• Strong programming experience in Python for data analysis, statistical modeling, and machine learning.

• Experience working with cloud platforms and modern data ecosystems, including AWS, Google Cloud Platform (GCP), Databricks, Snowflake, or similar technologies.

• Experience applying statistical techniques such as experimental design, hypothesis testing, statistical inference, regression analysis, causal inference and impact measurement

• Experience developing, validating, deploying, and monitoring machine learning models in production environments.

• Experience creating dashboards and visualizations using Tableau, Power BI, or comparable tools.

• Strong communication skills with the ability to explain technical concepts and analytical findings to non-technical audiences.

Preferred Qualifications

• Experience in the insurance industry, including claims, underwriting, actuary, or operational analytics.

• Experience supporting Agile teams and working in a sprint-based development environment.

• Experience with automation, agentic workflows, and AI-driven business solutions.

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