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Remote Topological Data Analysis Jobs in Michigan

OpTech

Analytics Engineer/Data Analyst

East Lansing, MI · On-site +1

Analytics Engineer/Data Analyst (W-2 Contract / No Agencies) We are seeking an Analytics Engineer ... remote work options, growth opportunities, 401K and competitive pay. Apply today! By submitting ...

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Remote Topological Data Analysis information

What are the key skills and qualifications needed to thrive as a Remote Topological Data Analyst, and why are they important?

To excel as a Remote Topological Data Analyst, you need a robust background in mathematics, particularly topology and data analysis, often supported by an advanced degree in mathematics, statistics, or computer science. Familiarity with programming languages such as Python or R, experience with TDA libraries (like GUDHI or Ripser), and proficiency in data visualization tools are typically required. Strong problem-solving skills, self-motivation, and effective communication are crucial soft skills for collaborating remotely and conveying complex insights to diverse stakeholders. These abilities enable analysts to extract meaningful patterns from high-dimensional data and deliver actionable results in a distributed work environment.

What are some typical challenges faced by professionals working in remote Topological Data Analysis roles?

Professionals in remote Topological Data Analysis often encounter challenges related to collaborating across distributed teams, especially when interpreting complex, high-dimensional data. Communication is key, as team members may need to frequently discuss data structures, algorithms, and findings via virtual meetings and shared documentation. Additionally, staying updated on the latest research and software tools in the rapidly evolving field requires proactive self-learning and participation in online communities. Despite these challenges, remote work offers flexibility and the opportunity to collaborate with experts worldwide.

What is remote topological data analysis?

Remote topological data analysis refers to the use of topological methods for analyzing data, performed by professionals who work remotely rather than on-site. Topological data analysis (TDA) is a technique in data science that uses concepts from topology, a branch of mathematics, to extract meaningful patterns and structures from complex datasets. Remote TDA specialists utilize tools like persistent homology to identify features such as clusters, holes, or voids in high-dimensional data. They often collaborate with teams virtually, leveraging cloud-based computational resources and software platforms to conduct their analyses. This approach allows organizations to access specialized expertise regardless of geographic location.
What are the most commonly searched types of Topological Data Analysis jobs in Michigan? The most popular types of Topological Data Analysis jobs in Michigan are:
What are popular job titles related to Remote Topological Data Analysis jobs in Michigan? For Remote Topological Data Analysis jobs in Michigan, the most frequently searched job titles are:
What job categories do people searching Remote Topological Data Analysis jobs in Michigan look for? The top searched job categories for Remote Topological Data Analysis jobs in Michigan are:
What cities in Michigan are hiring for Remote Topological Data Analysis jobs? Cities in Michigan with the most Remote Topological Data Analysis job openings:
Remote Topological Data Analysis jobs near you
Infographic showing various Remote Topological Data Analysis job openings in Michigan as of June 2026, with employment types broken down into 43% Full Time, and 57% Contract. Highlights an 100% Remote job distribution.
Contingent Summer Research Analyst Intern

Contingent Summer Research Analyst Intern

Arbor Research Collaborative for Health

Ann Arbor, MI • Remote

$27/hr

Full-time

Posted 6 days ago

Job description

About the Project

The CKD study uses 5 years of structured EHR data from a large private nephrology practice with over 50,000 patients. The study aims to:

  • Build standardized, analysis-ready analytic files (SAFs) spanning 2021–2025
  • Assess feasibility of longitudinal data elements (labs, prescriptions, disease history)
  • Characterize CD patients using contemporary clinical and treatment data
  • Evaluate the availability of specific variables (imaging, genetics, family history) in unstructured clinical records

The intern will be embedded in an active project team that includes biostatisticians, epidemiologists, data scientists, and clinical nephrologists, and will contribute to analytic work from day one.

Key Responsibilities
  • Contribute to construction and QC of longitudinal electronic health record (EHR) analytic files using structured data
  • Conduct descriptive analyses of patient demographics, lab values, medication use, and clinical characteristics
  • Summarize data availability, follow-up patterns, and measurement frequency across CKD subgroups
  • Support feasibility assessments by generating counts, proportions, and distributional summaries
  • Produce clean, well-documented analytic code and contribute to draft tables and figures
  • Participate in biweekly internal team meetings and client meetings, and contribute to written deliverables
Qualifications

Required:

  • Currently enrolled in a graduate program (MPH, MS, PhD, or equivalent) in biostatistics, epidemiology, data science, health informatics, or a related field
  • Proficiency in Python, R, or SAS for data manipulation and descriptive analysis
  • Comfort working with big data – large, messy, real-world datasets
  • Strong attention to detail and ability to write clean, reproducible, well-commented code
  • Ability to work independently with remote supervision
  • Comfort using AI-assisted coding tools (e.g., Claude, GitHub Copilot)

Preferred:

  • Familiarity with EHR data or claims-based data
  • Experience with longitudinal data structures (e.g., repeated lab measurements, time-to-event)
  • Experience with version control (Git)
Position Details
  • Duration: approximately July 1 – August 29, 2026 (flexible start; contingent on contract execution)
  • Hours: full-time (~40 hrs/week) or near full-time
  • Location: fully remote; no travel required
  • Compensation: paid internship (rate commensurate with experience)
  • Supervisor: Brian Bieber, MS, Research Scientist, Data Science
How to Apply

Submit a CV and a brief cover letter (1 page max) describing your relevant experience and availability. Applications will be reviewed on a rolling basis — early submission is strongly encouraged given the July start date.

Pay

$27 USD per hour

Apply