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Remote Environmental Data Scientist Jobs in Michigan

Ford Motor Company

Data Scientist

Dearborn, MI · Remote

... environments. * Bachelor's degree in Data Science, Engineering, Mathematics, Computer Science ... Benefit Summary This role is remote but if you live within 50 miles within Dearborn, MI, you will ...

Munich Re

Data Scientist

Ann Arbor, MI · On-site +1

... flexible environment that helps them succeed both at work and at home. Join our dynamic ... In this role, you'll collaborate closely with senior data scientists to build and support AI/ML and ...

Stryker

$35/hr

Opportunities Available As a Real-World Evidence Data Science intern at Stryker, you will: * Work ... learn in a dynamic environment Preferred: * Prior exposure to claims databases (Medicare ...

Stryker

$35/hr

Opportunities Available As a Real-World Evidence Data Science intern at Stryker, you will: * Work ... learn in a dynamic environment Preferred: * Prior exposure to claims databases (Medicare ...

Stryker

$35/hr

Opportunities Available As a Real-World Evidence Data Science intern at Stryker, you will: * Work ... learn in a dynamic environment Preferred: * Prior exposure to claims databases (Medicare ...

Stryker

$35/hr

Opportunities Available As a Real-World Evidence Data Science intern at Stryker, you will: * Work ... learn in a dynamic environment Preferred: * Prior exposure to claims databases (Medicare ...

Stryker

$35/hr

Opportunities Available As a Real-World Evidence Data Science intern at Stryker, you will: * Work ... learn in a dynamic environment Preferred: * Prior exposure to claims databases (Medicare ...

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All JobsEnvironmental Data Scientist JobsRemote Environmental Data Scientist JobsRemote Environmental Data Scientist Jobs in Michigan

Remote Environmental Data Scientist information

What is a remote environmental data scientist?

A Remote Environmental Data Scientist is a professional who analyzes and interprets environmental data, such as climate metrics, pollution levels, or ecosystem health, while working from a location outside of a traditional office setting. They use statistical tools, programming languages, and data visualization techniques to draw insights and support environmental projects or policy decisions. This role often involves collaborating with teams virtually, accessing remote databases, and communicating findings to stakeholders. Remote Environmental Data Scientists play a crucial role in addressing environmental challenges by providing actionable data-driven insights.

What are the key skills and qualifications needed to thrive as a remote environmental data scientist?

To thrive as a Remote Environmental Data Scientist, you need a strong background in statistics, environmental science, and data analysis, usually supported by a degree in a relevant field. Proficiency in programming languages like Python or R, GIS software, and experience with remote sensing data are typically required, along with certifications in data science or environmental analysis. Excellent problem-solving, communication, and self-motivation are crucial soft skills for collaborating remotely and translating data insights into actionable environmental solutions. These abilities are essential for accurately analyzing complex environmental data, informing policy, and driving sustainable initiatives from a remote setting.

How does a remote environmental data scientist typically collaborate with cross-functional teams while working offsite?

Remote Environmental Data Scientists often work closely with diverse teams, such as field researchers, software developers, and policy analysts, using digital collaboration tools like Slack, Zoom, and cloud-based data platforms. Effective communication and regular virtual meetings are essential for aligning on project goals, sharing data findings, and integrating insights into actionable recommendations. While remote work provides flexibility, it also requires strong self-management and proactive outreach to ensure seamless collaboration and data integrity across geographically dispersed teams.

What is the difference between Remote Environmental Data Scientist vs Remote Environmental Analyst?

AspectRemote Environmental Data ScientistRemote Environmental Analyst
Required CredentialsBachelor's or Master's in Environmental Science, Data Science, or related fields; proficiency in data analysis toolsBachelor's in Environmental Science, Geography, or related fields; basic data analysis skills
Work EnvironmentPrimarily remote, involving data modeling, analysis, and programmingPrimarily remote, focusing on data collection, reporting, and environmental assessments
Employer & Industry UsageUsed by environmental consulting firms, research institutions, and government agencies for data-driven decision makingEmployed by environmental agencies, NGOs, and consulting firms for environmental monitoring and reporting

The main difference is that Remote Environmental Data Scientists focus on advanced data analysis, modeling, and programming to interpret environmental data, while Remote Environmental Analysts concentrate on collecting data, preparing reports, and supporting environmental assessments. Both roles are essential in environmental sectors but differ in technical complexity and daily tasks.

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

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

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

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

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

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

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

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

Remote Environmental Data Scientist jobs near you

Data Scientist

Ford Motor Company

Dearborn, MI • Remote

Full-time

Medical, Dental, Vision, Life, PTO

Re-posted 25 days ago

Ford Motor Company rating

7.6

Company rating: 7.6 out of 10

Based on 528 frontline employees who took The Breakroom Quiz

11th of 45 rated automakers

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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