Healthcare Data Scientist

Summary

Join Mindoula as an early career Data Scientist and play a critical role in expanding our analytics capabilities. You will derive data-driven insights from diverse healthcare data sources, contributing to a small but growing team. This dynamic role offers ample opportunities for growth and impact. Responsibilities include processing and analyzing structured and unstructured healthcare data, maintaining data pipelines, investigating data issues, collaborating on analyses and modeling, and preparing reports for internal and client use. The ideal candidate possesses foundational healthcare analytics knowledge, strong SQL skills, and a commitment to data accuracy. This is a fully remote position within the United States.

Requirements

• 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
• 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

Responsibilities

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

Preferred Qualifications

Familiarity with causal inference methods preferred (e.g., difference-in-differences, propensity matching)