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Senior Data Annotation Analyst Jobs in Michigan (NOW HIRING)

Mariana Minerals

Sr. Data Engineer

Ann Arbor, MI · On-site

$103K - $140K/yr

They are seeking a Senior Data Engineer to design and build data pipelines that ensure the ... analytical results--including schema design, orchestration, reliability, and the contract it ...

Mariana Minerals

Sr. Data Engineer

Ann Arbor, MI · On-site

$103K - $140K/yr

They are seeking a Senior Data Engineer to design and manage data pipelines and architecture that ... analytical results--including schema design, orchestration, reliability, and the contract it ...

Accenture

Data Protection Sr. Analyst

Detroit, MI · Hybrid

$84K - $100K/yr

As a Data Protection Senior Analyst, you'll support the delivery of data protection, data governance, and privacy solutions for Avanade clients. You'll work under the guidance of senior team members ...

Cognizant Technologies

MI · On-site

$102K - $139K/yr

Duration : 1 year Qualifications The Senior Data Engineer will implement data-oriented solutions to ... Leads analysis, model and design the application data structure, storage, integration, deployment ...

Domino's

Sr. Data Scientist (REF39029K)

Ann Arbor, MI · On-site

POSITION TITLE: Sr. Data Scientist POSITION LOCATION: 30 Frank Lloyd Wright Dr., Ann Arbor, MI ... Analyze market segmentation and pricing strategies to identify opportunities to improve the ...

General Motors

Senior Data Engineer

Warren, MI

$99K - $135K/yr

Data scientists are expected to apply analytical and machine learning techniques, explore and ... 7 senior individual contributor role. This job may be eligible for relocation benefits.

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Senior Data Annotation Analyst information

What are Senior Data Annotation Analysts?

Senior Data Annotation Analysts are experienced professionals who oversee the process of labeling and tagging data, such as text, images, or audio, to train machine learning models. They are responsible for ensuring high-quality annotations, developing guidelines, and often mentoring junior annotators. Their work is crucial for the success of AI and machine learning projects, as accurate data annotation directly impacts model performance. Senior analysts also collaborate with data scientists and engineers to refine annotation processes and improve data quality.

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

To thrive as a Senior Data Annotation Analyst, you need expertise in data labeling, analytical thinking, and a strong understanding of machine learning concepts, often supported by a relevant degree or significant experience in data operations. Familiarity with annotation platforms (like Labelbox or Supervisely), data management tools, and quality assurance processes is typically required. Attention to detail, problem-solving, and the ability to communicate feedback effectively are crucial soft skills for this role. These competencies ensure high-quality data sets that drive accurate machine learning models and improve project outcomes.

How does a Senior Data Annotation Analyst typically collaborate with machine learning engineers and data scientists?

As a Senior Data Annotation Analyst, you will often work closely with machine learning engineers and data scientists to ensure that labeled data meets project requirements and quality standards. You may participate in meetings to discuss annotation guidelines, clarify ambiguous cases, and provide feedback on data challenges that arise. Your expertise in annotation tools and processes helps streamline workflows and ensures that the annotated datasets are reliable, which is critical for model training and evaluation. Collaboration is key, and you'll be expected to communicate effectively across teams to address issues and continuously improve the data pipeline.

What is the difference between Senior Data Annotation Analyst vs Data Annotation Specialist?

AspectSenior Data Annotation AnalystData Annotation Specialist
CredentialsBachelor's degree in related field, experience in data annotationHigh school diploma or equivalent, entry-level experience
Work EnvironmentCollaborative teams, project management, quality assuranceIndividual tasks, data labeling, basic quality checks
Industry UsageTech, AI, machine learning companiesAI startups, data labeling firms, research projects

The Senior Data Annotation Analyst typically has more experience, handles complex annotation projects, and oversees quality control, whereas the Data Annotation Specialist focuses on basic labeling tasks. Both roles are essential in AI data preparation, but the senior analyst often leads projects and ensures standards are met.

What are the most commonly searched types of Data Annotation Analyst jobs in Michigan? The most popular types of Data Annotation Analyst jobs in Michigan are:
What are popular job titles related to Senior Data Annotation Analyst jobs in Michigan? For Senior Data Annotation Analyst jobs in Michigan, the most frequently searched job titles are:
What job categories do people searching Senior Data Annotation Analyst jobs in Michigan look for? The top searched job categories for Senior Data Annotation Analyst jobs in Michigan are:
What cities in Michigan are hiring for Senior Data Annotation Analyst jobs? Cities in Michigan with the most Senior Data Annotation Analyst job openings:
Senior Data Annotation Analyst jobs near you
Infographic showing various Senior Data Annotation Analyst job openings in Michigan as of June 2026, with employment types broken down into 67% Full Time, and 33% Contract. Highlights an 100% In-person job distribution.

Sr. Data Engineer

Mariana Minerals

Ann Arbor, MI • On-site

$103K - $140K/yr

Full-time

Posted 5 days ago

Job description

Job Summary:
Mariana Minerals is a software-first, vertically integrated minerals company on a mission to supply the critical minerals powering modern energy, AI, and defense technologies. They are seeking a Senior Data Engineer to design and build data pipelines that ensure the reliability and queryability of plant data, supporting autonomous mineral refining operations. The role involves working across various data domains and collaborating with machine learning engineers to enhance production processes.
Responsibilities:
• Work across domains—for example, all plant sensor and historian data, or all lab and analytical results—including schema design, orchestration, reliability, and the contract it exposes to everyone downstream.
• Design and evolve our fleet of pipelines that pull from messy industrial sources—sensors, lab systems, historians, imagery, and more—into our databases and warehouse.
• Model time-series and analytical plant data for both human analysis and machine learning training, validation, and monitoring; own data quality, observability, and lineage in your domain.
• Build the data architecture that feeds production ML—the training and monitoring layer—in partnership with the ML engineers who own the model-specific semantics.
• Mentor earlier-career engineers and define the data contracts other teams build against.
• Work the boundary with machine learning deliberately: you own the platform and the interface it exposes; ML engineers own the features and models built on top of it. The training and monitoring layer is shared ground you design together.
Qualifications:
Required:
• 4+ years in data engineering or a closely related role.
• Strong Python and SQL, with deep experience designing database and warehouse schemas, including time-series and/or analytical data.
• Proven experience building reliable, orchestrated data pipelines and operating them in the cloud with containers and CI/CD.
• Experience with data quality, observability, and lineage, and comfort with messy real-world sources—drifting sensors, malformed exports, and the quirks of industrial systems.
• A self-starter comfortable in high-ambiguity environments, working directly with process engineers, ML engineers, and operations teams.
Preferred:
• Bonus: experience feeding data to ML systems—training datasets, feature pipelines, model monitoring—or working with industrial, sensor, or historian data.
Company:
Mariana Minerals is a software-first, vertically integrated minerals company focused on supplying the minerals critical to modern energy, AI, and defense technologies. Founded in , the company is headquartered in San Francisco, CA, US, , with a team of 51-200 employees. The company is currently Growth Stage.

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