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Data Annotation Services Jobs in Wisconsin (NOW HIRING)

AgSource

Sr. Data Engineer

Madison, WI · On-site

$114.10K - $137.10K/yr

Enable self-service analytics for R&D and product teams by exposing well- governed, documented data ... control, annotation, genotype imputation, genomic evaluation) using cloud and Databricks ...

Urus Group LP

Sr. Data Engineer

Madison, WI

$115.40K - $138.50K/yr

Enable self-service analytics for R&D and product teams by exposing well- governed, documented data ... control, annotation, genotype imputation, genomic evaluation) using cloud and Databricks ...

Urus

Sr. Data Engineer

Madison, WI

$114.10K - $137.10K/yr

Enable self-service analytics for R&D and product teams by exposing well- governed, documented data ... control, annotation, genotype imputation, genomic evaluation) using cloud and Databricks ...

Farm Systems

Sr. Data Engineer

Madison, WI

$114.10K - $137.10K/yr

Enable self-service analytics for R&D and product teams by exposing well- governed, documented data ... control, annotation, genotype imputation, genomic evaluation) using cloud and Databricks ...

Trans Ova Genetics

Sr. Data Engineer

Madison, WI · On-site

$115.40K - $138.50K/yr

Enable self-service analytics for R&D and product teams by exposing well- governed, documented data ... control, annotation, genotype imputation, genomic evaluation) using cloud and Databricks ...

AgSource

Sr. Data Engineer

Madison, WI · On-site

$114.10K - $137.10K/yr

Enable self-service analytics for R&D and product teams by exposing well- governed, documented data ... control, annotation, genotype imputation, genomic evaluation) using cloud and Databricks ...

Lorven Technologies

SAP SME Consultant - ABAP

Neenah, WI · On-site

$63.25 - $85.75/hr

Core Data Services annotations * Business Object Processing Framework (BOPF) Data Handling ... Business logic annotation and transformation rules * Functional-to-technical traceability

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Data Annotation Services information

What are the key skills and qualifications needed to thrive in Data Annotation Services, and why are they important?

To excel in Data Annotation Services, strong attention to detail, data literacy, and a foundational understanding of data labeling processes are essential, often requiring a high school diploma or equivalent. Familiarity with annotation platforms, labeling tools, and sometimes basic knowledge of scripting or data management systems is typically expected. Strong work ethic, consistency, and effective communication skills help individuals stand out in collaborative, deadline-driven environments. These capabilities ensure high-quality, accurate labeled data, which is critical for training reliable machine learning models.

What are some common challenges faced when working in data annotation services, and how can I address them?

In data annotation services, one common challenge is maintaining consistency and accuracy, especially when handling large datasets or ambiguous data points. Clear annotation guidelines and regular communication with team leads help ensure that everyone interprets the data similarly. Additionally, repetitive tasks can lead to fatigue, so it's important to take scheduled breaks and leverage available annotation tools to streamline workflows. Collaborating with peers to discuss edge cases also helps improve overall data quality and fosters a supportive team environment.

What are data annotation services?

Data annotation services involve labeling or tagging data—such as images, text, audio, or video—to make it understandable for machine learning models. These services are essential in training artificial intelligence systems to recognize patterns, objects, or other relevant information in raw data. Companies use data annotation to improve the accuracy and effectiveness of AI applications, such as self-driving cars, chatbots, and image recognition. Professional annotators or specialized platforms often perform these tasks to ensure high-quality, consistent results.

What is the difference between Data Annotation Services vs Data Labeling Specialists?

AspectData Annotation ServicesData Labeling Specialists
CredentialsTypically no formal credentials required; focus on trainingOften have training in specific tools or industry standards
Work EnvironmentCollaborative, often remote or in-office teamsSimilar, working in teams or independently on labeling tasks
Industry UsageUsed by AI/ML companies for training datasetsEmployed in similar settings, focusing on labeling data for AI models
Search & Comparison IntentUnderstanding services offered for data preparationLooking for roles or tasks related to data labeling

Data Annotation Services encompass the broader process of preparing and annotating data for AI and machine learning projects, often provided by specialized companies. Data Labeling Specialists are individual professionals or team members who perform the actual labeling tasks within these services. While both are closely related, services refer to the overall offering, whereas specialists are the personnel executing the work.

What are popular job titles related to Data Annotation Services jobs in Wisconsin? For Data Annotation Services jobs in Wisconsin, the most frequently searched job titles are:
What job categories do people searching Data Annotation Services jobs in Wisconsin look for? The top searched job categories for Data Annotation Services jobs in Wisconsin are:
What cities in Wisconsin are hiring for Data Annotation Services jobs? Cities in Wisconsin with the most Data Annotation Services job openings:
Data Annotation Services jobs near you
Sr. Data Engineer

Sr. Data Engineer

AgSource

Madison, WI • On-site

$114.10K - $137.10K/yr

Full-time

Posted yesterday

Job description

Job Description
This role is responsible for the design, development, and maintenance of data integration, analytics, and reporting solutions that support our animal genetics and bioinformatics workloads. The ideal candidate will possess expertise in Databricks and modern data engineering tools such as Azure Data Factory, combined with hands on experience working with biological, genomic, or other omics datasets. This position requires a proactive, self-motivated, and results-oriented individual with a passion for data, a strong understanding of data architecture and warehousing principles, and an appreciation for bioinformatics workflows in a commercial genetics environment.
Responsibilities
Data Integration
  • Design, develop, and maintain robust and efficient ETL/ELT pipelines and processes on Databricks for both operational and bioinformatics datasets (e.g., genomic markers, phenotypic data, laboratory outputs).
  • Ingest, transform, and harmonize structured and semi-structured biological data from lab systems, LIMS, sequencing platforms, and external partners into the enterprise data platform.
  • Troubleshoot and resolve Databricks pipeline errors and performance issues.
  • Optimize data flow performance and minimize data latency across scientific and business use cases.
  • Implement data quality checks, validations, and reconciliation processes within ETL workflows, including domain-specific checks for genomic and phenotypic data.

Databricks Development
  • Develop and maintain Databricks pipelines, notebooks, and datasets using Python, Spark, and SQL.
  • Optimize Databricks jobs for performance and cost-effectiveness, including largescale bioinformatics and analytics workloads.
  • Integrate Databricks with other data sources and systems, including lab instruments, genomic databases, and on-prem or cloud data stores.
  • Participate in the design and implementation of data lake architectures that support both traditional analytics and bioinformatics pipelines.

Data Warehousing
  • Participate in the design and implementation of data warehousing solutions to support reporting, analytics, and scientific modeling.
  • Model and curate subject areas for genetics, reproduction, and bioinformatics (e.g., animals, pedigrees, genotypes, breeding values, trials).
  • Support data quality initiatives and implement data cleansing procedures across business and scientific domains.

Reporting and Analytics
  • Collaborate with business users, scientists, geneticists, and bioinformaticians to understand data requirements for department-driven reporting and analytics needs.
  • Maintain and extend the existing library of complex dashboards and visualizations to surface genetic, reproductive, and operational insights.
  • Enable self-service analytics for R&D and product teams by exposing well- governed, documented data products.
  • Troubleshoot and resolve report issues, including performance bottlenecks and data inconsistencies.

Cloud Platform Experience
  • Apply strong programming skills in Python, SQL, and Spark to build scalable data and bioinformatics workflows.
  • Use CI/CD and IaC tools (Terraform, ARM, CloudFormation) to automate deployment of data platform components and analytics environments.
  • Design and implement Databricks platform architecture on Azure and AWS infrastructure, including environments that support largescale scientific computation.
  • Contribute to cloud security, governance, and cost optimization practices for data and bioinformatics workloads.

Bioinformatics and Scientific Collaboration
  • Partner with geneticists, biostatisticians, and bioinformaticians to translate scientific requirements into scalable data and platform architectures.
  • Support or orchestrate bioinformatics pipelines (e.g., variant processing, quality control, annotation, genotype imputation, genomic evaluation) using cloud and Databricks capabilities.
  • Ensure that data models, pipelines, and storage structures meet the needs of downstream analytics, predictive models, and genetic evaluations.
  • Advocate for best practices in managing sensitive biological and genetic data, including data governance, access control, and compliance with relevant standards and regulations.

Collaboration and Communication
  • Thrive in an entrepreneurial, self-starting, and fast-paced environment, working both independently and with our highly skilled teams.
  • Collaborate effectively with business users, data analysts, scientists, and other IT teams.
  • Communicate technical information clearly and concisely, both verbally and in writing, to technical and nontechnical stakeholders.
  • Document all development work, data models, and procedures thoroughly, including bioinformatics and scientific data flows.

Continuous Growth
  • Keep abreast of the latest advancements in data integration, cloud platforms, bioinformatics tooling, and data engineering technologies.
  • Continuously improve skills and knowledge through training and self-learning in both data engineering and bioinformatics domains.

Requirements
  • Bachelor's degree in Computer Science, Information Systems, Bioinformatics, Computational Biology, or a related field; a Master's degree is an asset.
  • 7+ years of experience in data integration and reporting, with experience designing and operating cloud-based data platforms.
  • Extensive experience with Databricks, including Python, Spark, and Delta Lake.
  • Strong proficiency with relational databases (e.g., SQL Server, RDS), including TSQL, stored procedures, and functions.
  • Experience with data warehousing concepts and best practices.
  • Experience with Microsoft Azure cloud platform; exposure to Microsoft Fabric is desirable.
  • Hands on experience working with biological, genomic, or other omics datasets in a bioinformatics or life sciences setting (e.g., sequence data, SNP arrays, GWAS outputs, phenotypic traits).
  • Familiarity with common bioinformatics tools, data formats (e.g., FASTQ, VCF, PLINK), and workflows is highly desirable.
  • Strong analytical and problem-solving skills, with the ability to reason about complex data and scientific requirements.
  • Excellent communication and interpersonal skills.
  • Ability to work independently and as part of a cross-functional team across IT, science, and business.
  • Experience with Agile methodologies.
  • Demonstrated background in bioinformatics or computational biology, preferably supporting genetics, breeding, or life science research in an applied or commercial context.
  • Must be legally authorized to work in the United States.

About Us
As a holding company with cooperative and private ownership, URUS is a family of businesses at the heart of the dairy and beef industry - Alta Genetics, GENEX, Genetics Australia, Leachman Cattle, Jetstream, PEAK, SCCL, Trans Ova Genetics and VAS. Each organization has its unique identity, products, and services. These companies work globally to provide cutting-edge dairy and beef genetics, customized reproductive services to maximize conceptions, dairy management information to take producers to the frontline of progressive dairy farming, and an array of products and services to help bovines reach their full genetic potential. URUS has 9 brands in 17 retail countries and employs nearly 2,800 people globally.

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