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Remote Data Management Jobs in Michigan (NOW HIRING)

Ford Motor Company

Data Scientist

Dearborn, MI · Remote

... management leases Tuition assistance Established and active employee resource groups Paid time off ... Benefit Summary This role is remote but if you live within 50 miles within Dearborn, MI, you will ...

CIBC US

$130K - $150K/yr

As a Data Security Consultant, you'll play a key role in shaping and maturing data security ... flexibility to manage your work activities within a remote (if non-local) or hybrid work ...

CIBC US

Data Security Consultant

Three Rivers, MI · On-site +1

$130K - $150K/yr

As a Data Security Consultant, you'll play a key role in shaping and maturing data security ... flexibility to manage your work activities within a remote (if non-local) or hybrid work ...

CIBC

Data Security Consultant

Three Rivers, MI · On-site +1

$130K - $150K/yr

As a Data Security Consultant, you'll play a key role in shaping and maturing data security ... flexibility to manage your work activities within a remote (if non-local) or hybrid work ...

CIBC

$130K - $150K/yr

As a Data Security Consultant, you'll play a key role in shaping and maturing data security ... flexibility to manage your work activities within a remote (if non-local) or hybrid work ...

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Remote Data Management information

What is remote data management?

Remote Data Management refers to the practice of organizing, storing, securing, and analyzing data from a location outside of an organization's physical premises. Professionals in this field use cloud-based tools and remote access software to manage databases, ensure data integrity, and facilitate data sharing among distributed teams. This approach enables businesses to maintain their data infrastructure efficiently while supporting remote work and global collaboration. It also involves implementing protocols to ensure data security and compliance with relevant regulations.

What are remote jobs in data management?

Various types of remote data management jobs are available in healthcare and insurance, with titles like clinical data manager, analyst, and auditor. In these jobs, you manage a database and provide analysis of information related to clinical trials, medical payments, operational activities, and more. Your duties and responsibilities include generating reports, database administration, and providing support for coworkers throughout your organization, but your responsibilities can vary based on what kinds of data you store. Additionally, you work with your company’s leadership to address issues affecting the availability and use of data and proactively suggest strategies for improving efficiency and accuracy. In some roles, you also participate in project planning, resource allocation, and budgeting.

What are the key skills and qualifications needed to thrive in remote data management?

To thrive in Remote Data Management, you need strong analytical skills, attention to detail, and a solid understanding of database concepts, often supported by a degree in information systems or a related field. Proficiency with data management tools such as SQL, Microsoft Excel, and cloud-based platforms like AWS or Google Cloud, as well as knowledge of data privacy regulations, is typically required. Excellent communication, self-motivation, and time management are important soft skills for collaborating with distributed teams and managing tasks independently. These skills ensure accurate, secure, and efficient handling of data assets while maintaining productivity and compliance in a remote work environment.

What are some common challenges faced in remote data management, and how can they be addressed?

One common challenge in remote data management is ensuring data security and integrity while working outside a traditional office environment. Remote team members must follow strict protocols for data access and regularly update security measures to protect sensitive information. Additionally, effective communication and collaboration with cross-functional teams can be more difficult remotely, so utilizing project management and communication tools is vital. Establishing clear workflows and regular check-ins helps maintain data consistency and team alignment.

What is the difference between Remote Data Management vs Data Analyst?

AspectRemote Data ManagementData Analyst
CredentialsTypically requires a degree in IT, Computer Science, or related fields; certifications like Microsoft Certified Data Analyst or AWS Data AnalyticsRequires a degree in Statistics, Mathematics, or related fields; certifications like Microsoft Certified Data Analyst Associate or Tableau Desktop Specialist
Work EnvironmentPrimarily remote, working with databases, data warehouses, and cloud platformsRemote or on-site, analyzing data sets, creating reports, and visualizations
Industry UsageUsed across IT, finance, healthcare, and e-commerce sectorsCommon in finance, marketing, healthcare, and consulting industries

Remote Data Management and Data Analysts share overlapping skills in data handling and analytics, but differ mainly in focus. Remote Data Management emphasizes database administration and cloud platforms, while Data Analysts focus on interpreting data and creating insights. Both roles are vital in data-driven organizations and often collaborate to optimize data use.

More about Remote Data Management jobs

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

The most popular types of Data Management jobs in Michigan are:

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

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

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

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

What cities in Michigan are hiring for Remote Data Management jobs?

Cities in Michigan with the most Remote Data Management job openings:

Infographic showing various Remote Data Management job openings in Michigan as of September 2026, with employment types broken down into 1% As Needed, 82% Full Time, 11% Part Time, and 6% Contract. Highlights an 82% Physical, 2% Hybrid, and 16% Remote job distribution.

Data Scientist

Dearborn, MI • Remote

Ford Motor Company
Motor Vehicle Manufacturing • 10K+ employees
Learn more about Ford Motor Companyexternal

Full-time

Medical, Dental, Vision, Life, PTO

Re-posted 3 days ago

Ford Motor Company rating

7.6

Company rating: 7.6 out of 10

Based on 530 frontline employees who took The Breakroom Quiz

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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