Healthcare Data Scientist

We are seeking an early career Data Scientist who will play a critical role in continuing and building on Mindoula’s analytics capabilities. The work will focus on providing data-driven insights from a wide range of healthcare data including structured and unstructured sources. This is a dynamic role where you will join a small but growing team, with plenty of opportunities to grow and contribute to our data capabilities. We are looking for someone who is eager to contribute, is extremely detail oriented, and who wants to make a difference in the patient populations we serve.

This role is ideal for someone with foundational knowledge of healthcare analytics, strong SQL skills, and a deep respect for data accuracy. You’ll support our efforts to measure program performance through return-on-investment (ROI) modeling, prepare accurate data presentations for clients, and ensure analytic outputs are reliable and trustworthy.

You’ll start out by running and maintaining pre-built notebooks, performing ad hoc data investigations, interpreting results, and flagging anomalies. You’ll populate client deliverables with precision, ensuring every number is correct. You’ll use your understanding of statistics and healthcare data to verify and triangulate results. If you have a strong grasp of SQL and relational databases, some Python or R experience, and care deeply about precision, we want to hear from you.

What You'll Do...

Work with Healthcare Data:

• Process and analyze structured and unstructured healthcare data, including claims, assessments, surveys, chat/call logs, and third-party sources.
• Maintain and improve data pipelines and modeling code for scalability, automation, and reproducibility.
• Investigate data issues and contribute to improvements in data quality and integrity.
  
  

Support Data Analysis & Modeling

• Collaborate with team members to conduct analyses and modeling workflows with guidance from senior staff.
• Execute and validate outputs from production notebooks and pipelines, flagging inconsistencies and escalating unusual findings.
• Apply statistical methods—including propensity score matching, difference-in-differences, and time series modeling—to evaluate program impact and calculate ROI.
• Perform ad-hoc and root cause analyses to uncover trends and support findings from evaluations.
  
  

Collaborate Across Functions

• Partner with the data science team to define scopes, participate in code reviews, and support ad-hoc analysis requests.
• Work cross-functionally with data engineering, analytics, and software teams on shared initiatives.
• Help maintain and update technical documentation and training materials.
  
  

Communicate Findings

• Prepare clear and accurate reports for internal use and client deliverables.
• Translate complex analytical findings into accessible insights for business and clinical stakeholders.
  
  

What You'll Need...

• Master’s degree required in quantitative field: epidemiology, psychology, health administration, public health, computer science, statistics, data science, economics, mathematics, engineering or related field. A bachelor’s degree in one of these fields with equivalent experience (typically 4+ additional years) will also be considered.
• Experience: 2-6 years experience (internship, specific coursework, and projects apply); experience with health-related data is required (e.g., claims, program data, EHR).
• Statistics coursework and/or training is required; familiarity with core statistical concepts including variance, relative risk, incidence/prevalence, etc.
• Strong foundation in programming languages including SQL, Python, and R.
• Familiarity with programming notebooks (e.g., Jupyter, Databricks) and tools like Pandas or NumPy or similar.
• Experience writing SQL to query relational databases (e.g., Athena, postgreSQL, MS Access).
• Proven ability to troubleshoot code, technical issues, and uncover root cause data issues.
• Familiarity with causal inference methods preferred (e.g., difference-in-differences, propensity matching).
• Strong attention to detail—able to spot inconsistencies or incorrect values in output.
• Excellent communication and interpersonal skills, with the ability to explain complex concepts to non-technical stakeholders.
• Enthusiasm for working in a collaborative, cross-functional team environment, paired with a proactive, problem-solving mindset.
• Comfort with working in a virtual, remote environment and leveraging virtual communication platforms.
  
  

Location...

This is a 100% remote position that must be located in the United States. Mindoula is not able to provide any employment sponsorship at this time.