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Senior Data Science Analytics Jobs in Madison, WI

AgSource

Sr. Data Engineer

Madison, WI · On-site

$114K - $137K/yr

Reporting and Analytics * Collaborate with business users, scientists, geneticists, and bioinformaticians to understand data requirements for department-driven reporting and analytics needs.

Urus

Sr. Data Engineer

Madison, WI · On-site

$114K - $137K/yr

Reporting and Analytics * Collaborate with business users, scientists, geneticists, and bioinformaticians to understand data requirements for department-driven reporting and analytics needs.

Wisconsin

IRP Research Analyst - BFY

Madison, WI

$55K - $70K/yr

Bachelor's degree, required Preference in social science, public/population health, data science/analytics, or related field. Master's Degree, preferred Preference in social science, public ...

Wisconsin

Data Scientist II or III

Madison, WI · On-site

$85K/yr

This role combines rigorous statistical analysis with modern data science practices, including use of GitHub for version control, collaboration, and reproducible analyses. The successful candidate ...

Wisconsin

Data Governance Program Manager

Madison, WI · On-site +1

The UW School of Medicine and Public Health (SMPH) is a leader in research and innovation, dedicated to improving patient outcomes through advanced data science and analytics. This position is within ...

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All JobsData Science Analytics JobsSenior Data Science Analytics JobsSenior Data Science Analytics Jobs in Madison, WI

Senior Data Science Analytics information

See Madison, WI salary details

$41.8K

$143.5K

$202.5K

How much do senior data science analytics jobs pay per year?

As of Aug 6, 2026, the average yearly pay for senior data science analytics in Madison, WI is $143,547.00, according to ZipRecruiter salary data. Most workers in this role earn between $119,400.00 and $167,800.00 per year, depending on experience, location, and employer.

What is the difference between Senior Data Science Analytics vs Data Analyst?

AspectSenior Data Science AnalyticsData Analyst
Required CredentialsBachelor's/Master's in Data Science, Statistics, or related fields; often requires experience in machine learningBachelor's degree in Data, Statistics, or related fields; often entry-level or junior roles
Work EnvironmentAdvanced analytics, predictive modeling, machine learning projectsData cleaning, reporting, basic analysis
Employer & Industry UsageTech companies, finance, healthcare, consulting firmsRetail, marketing, small businesses, entry-level roles across industries

Senior Data Science Analytics professionals focus on complex modeling and predictive analytics, often requiring advanced skills and experience. Data Analysts typically handle data collection, cleaning, and basic reporting. While both roles work with data, Senior Data Science Analytics roles involve more technical expertise and strategic insights, making them suitable for experienced professionals in data-driven industries.

What are the most commonly searched types of Data Science Analytics jobs in Madison, WI? The most popular types of Data Science Analytics jobs in Madison, WI are:
Senior Data Science Analytics jobs near you
Infographic showing various Senior Data Science Analytics job openings in Madison, WI as of August 2026, with employment types broken down into 67% Full Time, and 33% Temporary. Highlights an 67% In-person, and 33% Remote job distribution, with an average salary of $143,547 per year, or $69 per hour.

Sr. Data Engineer

AgSource

Madison, WI • On-site

$114K - $137K/yr

Full-time

This job post has expired today. Applications are no longer accepted.

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.

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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