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Remote Genomics Data Scientist Jobs in Michigan (NOW HIRING)

Ford Motor Company

Data Scientist

Dearborn, MI · Remote

The Senior Data Scientist will design and implement advanced systems that support cross-domain ... Benefit Summary This role is remote but if you live within 50 miles within Dearborn, MI, you will ...

CSAA

... Remote Your Role ... This Data Scientist role offers the opportunity to help CSAA Insurance Group anticipate and prepare ...

Varsity Tutors

Genetics Tutor

Detroit, MI · Remote

$18 - $40/hr

... genomic data. Emphasizes probabilistic reasoning and connects genetics to genetic counseling, forensic science, evolutionary biology, and personalized medicine applications. * Curriculum Awareness ...

Varsity Tutors

Genetics Tutor

Ann Arbor, MI · Remote

$18 - $40/hr

... genomic data. Emphasizes probabilistic reasoning and connects genetics to genetic counseling, forensic science, evolutionary biology, and personalized medicine applications. * Curriculum Awareness ...

Varsity Tutors

Genetics Tutor

Kalamazoo, MI · Remote

$18 - $40/hr

... genomic data. Emphasizes probabilistic reasoning and connects genetics to genetic counseling, forensic science, evolutionary biology, and personalized medicine applications. * Curriculum Awareness ...

Vervint

Director of Data Intelligence

Grand Rapids, MI · On-site +1

Director of Data Intelligence | Remote | Michigan or Minnesota Preferred Role Snapshot: * Set the ... Build and lead a high-performing team of data scientists, analysts, and engineers. * Promote a ...

Danaher

Principal Clinical Scientist

Detroit, MI · On-site +1

... remote based (can also be onsite in Chaska, MN). At Danaher Diagnostics, our vision is to be the ... Ability to critically review and interpret scientific and clinical trial data, and oversee the ...

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Remote Genomics Data Scientist information

What is a remote genomics data scientist?

A Remote Genomics Data Scientist is a professional who analyzes and interprets large-scale genetic data, such as DNA sequencing, using computational and statistical methods, while working from a remote location. Their work often involves using bioinformatics tools and machine learning to identify patterns and insights in genomic datasets. This role supports research in healthcare, pharmaceuticals, and biotechnology, helping to advance personalized medicine and disease understanding. Remote Genomics Data Scientists collaborate with multidisciplinary teams and often communicate findings through reports and visualizations.

How does a remote genomics data scientist typically collaborate with cross-functional research teams?

As a Remote Genomics Data Scientist, collaboration often happens through virtual meetings, shared code repositories, and cloud-based data platforms. You'll regularly interact with bioinformaticians, laboratory scientists, and clinicians to define research questions, interpret genomic data, and ensure data analysis aligns with study goals. Effective communication is key, as you'll translate complex results for non-technical stakeholders and coordinate project timelines across different locations and time zones.

What are the key skills and qualifications needed to thrive as a remote genomics data scientist, and why are they important?

To thrive as a Remote Genomics Data Scientist, you need a strong background in bioinformatics, statistics, and genomics, typically supported by an advanced degree in a relevant field. Proficiency with programming languages such as Python or R, experience with genomic data analysis tools (e.g., GATK, Bioconductor), and cloud-based data platforms are essential. Excellent problem-solving, communication, and collaboration skills help you interpret complex data and work effectively with interdisciplinary teams. These skills ensure accurate analysis, meaningful insights, and successful remote collaboration on cutting-edge genomics projects.

What is the difference between Remote Genomics Data Scientist vs Remote Bioinformatics Analyst?

AspectRemote Genomics Data ScientistRemote Bioinformatics Analyst
Required CredentialsMaster's or PhD in Genetics, Bioinformatics, or related field; experience with genomics data analysisBachelor's or Master's in Bioinformatics, Biology, or related; familiarity with bioinformatics tools
Work EnvironmentResearch labs, biotech companies, or healthcare organizations focusing on genomicsResearch institutions, healthcare, or biotech firms analyzing biological data
Industry UsagePrimarily in genomics research, personalized medicine, and biotechIn bioinformatics projects across healthcare, agriculture, and research

Remote Genomics Data Scientists focus on analyzing genomic data to uncover biological insights, often requiring advanced degrees and specialized skills. Remote Bioinformatics Analysts handle biological data analysis but may have a broader scope, including various biological datasets. Both roles are vital in biotech and healthcare industries, but the Genomics Data Scientist typically emphasizes genomic-specific expertise.

What are the most commonly searched types of Genomics Data Scientist jobs in Michigan?

The most popular types of Genomics Data Scientist jobs in Michigan are:

What are popular job titles related to Remote Genomics Data Scientist jobs in Michigan?

For Remote Genomics Data Scientist jobs in Michigan, the most frequently searched job titles are:

What job categories do people searching Remote Genomics Data Scientist jobs in Michigan look for?

The top searched job categories for Remote Genomics Data Scientist jobs in Michigan are:

What cities in Michigan are hiring for Remote Genomics Data Scientist jobs?

Cities in Michigan with the most Remote Genomics Data Scientist job openings:

Remote Genomics Data Scientist jobs near you
Infographic showing various Remote Genomics Data Scientist job openings in Michigan as of July 2026, with employment types broken down into 5% Internship, 86% Full Time, and 9% Part Time. Highlights an 100% Remote job distribution.

Data Scientist

Dearborn, MI • Remote

Ford Motor Company
Motor Vehicle Manufacturing • 10K+ employees

7.6

Company rating: 7.6 out of 10

Based on 525 frontline employees who took The Breakroom Quiz

13th of 45 rated automakers

People enjoy working here

Good employer

Recommended by students

Learn more about Ford Motor Companyexternal

Full-time

Medical, Dental, Vision, Life, PTO

Re-posted 14 days ago

Job description

The Senior Data Scientist will design and implement advanced systems that support cross-domain manufacturing analytics. This role operates at the intersection of optimization, enterprise data integration, and applied analytics to enable data-driven decision-making across complex business workflows.

Key responsibilities include:

  • Developing and maintaining Python-based optimization models to support demand elasticity, production planning, and constraint-based decision frameworks.

  • Integrating heterogeneous enterprise datasets into structured, analysis-ready pipelines using BigQuery, GCS, and Python.

  • Performing data reconciliation, fuzzy matching, and standardization across inconsistent source systems to ensure data quality and analytical integrity.

  • Designing and deploying lightweight internal applications (e.g., Dash-based tools) and contributing to containerized deployments to enable business-facing access to decision models.

  • Collaborating with cross-functional stakeholders to translate business questions into optimization and analytical frameworks.

In addition, this role will contribute to the development of semantically aligned data structures by supporting feature definition consistency, cross-system mapping, and ontology-informed modeling approaches. The candidate will help ensure that analytical outputs are built on clearly defined entities, relationships, and assumptions to enable scalable reasoning and reuse across domains.

The ideal candidate combines strong technical modeling capability with practical enterprise data engineering experience and the ability to operate effectively in ambiguous, cross-functional environments.

  • Bachelor's degree in Data Science, Engineering, Mathematics, Computer Science, Operations Research, or equivalent field.

  • 3+ years of experience developing analytical or optimization models in Python.

  • Experience building and maintaining data pipelines using SQL and cloud-based data platforms (e.g., BigQuery, GCS).

  • Strong proficiency in Python for data analysis and modeling (e.g., pandas, NumPy, Pyomo or similar optimization libraries).

  • Experience integrating and standardizing heterogeneous enterprise datasets.

  • Familiarity with containerization concepts (e.g., Docker) and deploying lightweight applications or services in a cloud environment.

  • Ability to translate business problems into structured analytical frameworks.

  • Strong written and verbal communication skills with experience working cross-functionally.

You may not check every box, or your experience may look a little different from what we've outlined, but if you think you can bring value to Ford Motor Company, we encourage you to apply!
As an established global company, we offer the benefit of choice. You can choose what your Ford future will look like: will your story span the globe, or keep you close to home? Will your career be a deep dive into what you love, or a series of new teams and new skills? Will you be a leader, a changemaker, a technical expert, a culture builder...or all of the above? No matter what you choose, we offer a work life that works for you, including:
Immediate medical, dental, vision and prescription drug coverage
Flexible family care days, paid parental leave, new parent ramp-up programs, subsidized back-up child care and more
Family building benefits including adoption and surrogacy expense reimbursement, fertility treatments, and more
Vehicle discount program for employees and family members and management leases
Tuition assistance
Established and active employee resource groups
Paid time off for individual and team community service
A generous schedule of paid holidays, including the week between Christmas and New Year's Day
Paid time off and the option to purchase additional vacation time.
 
For a detailed look at our benefits, click here: Benefit Summary
 
 
This role is remote but if you live within 50 miles within Dearborn, MI, you will be required on-site 4x a week.
 
*Visa Sponsorship IS provided for this specific role*
*Relocation assistance IS provided for this specific role*
 
Candidates for positions with Ford Motor Company must be legally authorized to work in the United States. Verification of employment eligibility will be required at the time of hire.
We are an Equal Opportunity Employer committed to a culturally diverse workforce. All qualified applicants will receive consideration for employment without regard to race, religion, color, age, sex, national origin, sexual orientation, gender identity, disability status or protected veteran status. In the United States, If you need a reasonable accommodation for the online application process due to a disability, please call 1-888-336-0660.
 
#LI-Remote
#LI-DS2 
 
SG7-8

  • Design, develop, and maintain Python-based optimization models to support demand elasticity, production planning, and constraint-based decision systems.

  • Translate complex business problems into structured analytical and optimization frameworks.

  • Build and maintain data pipelines using BigQuery, GCS, and Python to integrate heterogeneous enterprise data sources.

  • Perform data reconciliation, fuzzy matching, and standardization across inconsistent datasets to ensure analytical integrity.

  • Develop lightweight internal applications (e.g., Dash or streamlit) to operationalize analytical outputs for business users.

  • Contribute to containerized deployments to support scalable and maintainable delivery of decision tools.

  • Partner with cross-functional stakeholders to define requirements and validate outputs.

  • Support semantic alignment across systems by contributing to feature definition consistency, cross-system mapping, and ontology-informed data structures.

  • Document modeling assumptions, data transformations, and system dependencies to enable reproducibility and reuse.

  • Continuously improve model performance, data quality, and deployment efficiency across decision systems.

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