Data Scientist

About the Role Etactics is a healthcare clearinghouse processing millions of X12 transactions including claims, remits, eligibility, and authorizations for over 4,000 providers. We're hiring a Data Scientist to turn that data into real operational and financial impact. This is not a reporting role. You will build models, uncover insights, and deploy solutions that directly improve revenue cycle performance. What You'll Do Build machine learning models to improve: Claim acceptance, Denial prediction and prevention, Eligibility accuracy, Workflow efficiency Analyze large-scale healthcare transaction data (X12 and RCM workflows) Design datasets and data pipelines for modeling Partner with engineering to deploy models into production Develop tools and dashboards that drive action Identify inefficiencies, automation opportunities, and process improvements What You Bring Bachelor's degree in Data Science, Computer Science, or related field Experience with: Python (Pandas, Scikit-learn or similar), SQL or MySQL, Working with large datasets Experience building: Classification or regression models, Data pipelines or analytical workflows, Strong problem-solving skills and ability to work independently Nice to Have Healthcare or RCM experience (claims, EHR, clearinghouse) X12 knowledge (837, 835, 270/271) AWS, APIs, or backend systems Spark or large-scale data processing What Success Looks Like (Year 1) Built and deploy multiple production ready dashboards for C-Suite customers and end users. Improved a measurable KPI such as denials, efficiency, or revenue Data is used more proactively across the organization Why Etactics Real impact with work that directly affects revenue and customer outcomes Ownership from building through deployment Growth in a complex, data-rich domain Close collaboration with product and engineering Requirements Bachelor's degree or equivalent experience Hands-on experience with data analysis and programming

Pay:

From $75,000.00 per year

Benefits:

401(k) 401(k) matching Dental insurance Employee assistance program Flexible spending account Health insurance Health savings account Life insurance Paid time off Vision insurance Application Question(s): escribe a machine learning or data project you worked on. What was the problem, what approach did you take, and what was the measurable outcome? Have you ever taken a model or analysis into production or real-world use? If so, explain how it was used and what impact it had. Given a dataset of healthcare claims, how would you approach identifying and reducing claim denials?

Work Location:

In person