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Environmental Data Science Jobs in Iowa (NOW HIRING)

Sr Data Scientist

Des Moines, IA · On-site

$117K - $146K/yr

... environment. Demonstrated experience in team leadership, project planning and management, and stakeholder engagement. Partner with Product Owners to lead end-to-end data science initiatives, from ...

S. or above in Applied Statistics, Artificial Intelligence, Biostatistics, Computer Science, Data ... Git) • Ability to work both independently and within a multidisciplinary team environment to ...

Environmental Scientist

Des Moines, IA · On-site

$73K - $96K/yr

Join a team that has the environment down to a science. Our Environmental Services group is ... and analyzing data; writing technical reports for client and regulatory agency review; and ...

Environmental Scientist

Independence, IA · On-site

$71K - $93K/yr

Join a team that has the environment down to a science. Our Environmental Services group is ... and analyzing data; writing technical reports for client and regulatory agency review; and ...

Environmental Scientist

Independence, IA · On-site

$71K - $93K/yr

Join a team that has the environment down to a science. Our Environmental Services group is ... and analyzing data; writing technical reports for client and regulatory agency review; and ...

... data, and evaluating sustainability proposals. Emphasizes systems thinking and connects ... Familiar with AP Environmental Science curriculum across nine units and common challenges such as ...

Sr Data Engineer

Des Moines, IA · On-site

$111K - $134K/yr

Bachelor's degree in information systems, computer science or related technical field or equivalent ... environments. Architect, Design, and Deliver Scalable Data Pipelines * Lead the design and ...

Lead Data Engineer

Cedar Rapids, IA · On-site

$112K - $134K/yr

Work collaboratively with other engineers, data scientists, analytics teams, and business product owners in an agile environment: * Architect, build, and support the operation of Cloud and On ...

Lead Data Engineer

Cedar Rapids, IA · On-site

$112K - $134K/yr

Work collaboratively with other engineers, data scientists, analytics teams, and business product owners in an agile environment: * Architect, build, and support the operation of Cloud and On ...

Sr Data Engineer

Des Moines, IA · On-site

$117K - $146K/yr

... environments. Define Data Architecture, Modeling Standards, and Lakehouse Patterns * Design and ... Partner with data scientists, analysts, and business stakeholders to shape data strategy, clarify ...

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Environmental Data Science information

See Iowa salary details

$35.2K

$115.3K

$184.6K

How much do environmental data science jobs pay per year?

As of Jul 26, 2026, the average yearly pay for environmental data science in Iowa is $115,284.00, according to ZipRecruiter salary data. Most workers in this role earn between $92,500.00 and $127,700.00 per year, depending on experience, location, and employer.

What does an environmental data scientist do?

An environmental data scientist analyzes environmental data to identify patterns, assess environmental risks, and support decision-making. They use statistical tools, programming languages like Python or R, and GIS software to interpret data related to climate, pollution, and natural resources, often working with large datasets and models to inform environmental policies and practices.

Can data scientists make $300k?

Environmental data scientists can potentially earn $300,000 or more at senior levels or in specialized roles, especially with extensive experience, advanced skills in machine learning, and working in high-demand industries or organizations. However, such salaries are typically achieved through seniority, leadership positions, or in regions with higher compensation standards.

What is Environmental Data Science?

Environmental Data Science is an interdisciplinary field that uses statistical, computational, and analytical techniques to collect, analyze, and interpret large sets of data related to the environment. Professionals in this field work on issues like climate change, pollution, biodiversity, and natural resource management by extracting meaningful insights from complex environmental datasets. Their work supports decision-making for policy, conservation, and sustainability initiatives. Environmental data scientists often collaborate with ecologists, geographers, and policymakers to address environmental challenges using data-driven approaches.

Is 40 too late for data science?

Environmental Data Science is a field that values skills and experience over age, and many professionals transition into data science later in their careers. Gaining relevant knowledge in programming, statistics, and environmental data tools can enable a successful entry regardless of age, as continuous learning and practical experience are key factors in the field.

What are some common challenges faced by environmental data scientists when working with real-world datasets?

Environmental data scientists often encounter challenges such as incomplete or inconsistent data, varying data formats, and the need to integrate information from multiple sources like sensors, satellites, and field observations. Addressing missing values, data quality issues, and ensuring proper geospatial alignment can be time-consuming but is essential for producing reliable analyses. Collaboration with domain experts and stakeholders is frequently required to interpret findings and ensure that the results are actionable for environmental policy or management decisions.

What is the difference between Environmental Data Science vs Environmental Data Analyst?

AspectEnvironmental Data ScienceEnvironmental Data Analyst
Required CredentialsTypically requires a degree in data science, environmental science, or related fields; often includes programming and statistical certificationsUsually requires a degree in environmental science, geography, or related fields; may include basic data analysis certifications
Work EnvironmentResearch labs, data centers, environmental agencies, or consulting firmsEnvironmental agencies, research organizations, or consulting firms
Employer & Industry UsageUsed in environmental research, climate modeling, and policy analysisUsed in environmental monitoring, reporting, and data interpretation

Environmental Data Science focuses on developing models and algorithms to analyze complex environmental data, often requiring advanced programming skills. In contrast, Environmental Data Analysts primarily interpret and visualize environmental data to support decision-making. Both roles are vital but differ in technical depth and scope.

What is the highest paying environmental science job?

Environmental Data Science roles such as senior environmental data scientists or environmental analytics managers tend to have the highest salaries in the field, often exceeding $100,000 annually. These positions typically require advanced skills in data analysis, programming, and environmental modeling, and may involve leadership responsibilities or specialized expertise in areas like climate modeling or sustainability analytics.

What are the key skills and qualifications needed to thrive as an Environmental Data Scientist, and why are they important?

To thrive as an Environmental Data Scientist, you need strong quantitative skills, expertise in environmental science, and a relevant degree in data science, statistics, or a related field. Familiarity with data analysis tools such as Python, R, GIS software, and experience with large datasets or machine learning techniques is typical. Exceptional problem-solving abilities, communication skills, and attention to detail set top performers apart in this field. These competencies are crucial for effectively interpreting complex environmental data, informing policy, and driving impactful sustainability initiatives.
What are popular job titles related to Environmental Data Science jobs in Iowa? For Environmental Data Science jobs in Iowa, the most frequently searched job titles are:
Infographic showing various Environmental Data Science job openings in Iowa as of July 2026, with employment types broken down into 1% As Needed, 81% Full Time, 14% Part Time, 1% Temporary, and 3% Contract. Highlights an 86% Physical, 4% Hybrid, and 10% Remote job distribution, with an average salary of $115,284 per year, or $55.4 per hour.
Sr Data Scientist

$117K - $146K/yr

Full-time

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


Berkshire Hathaway Energy rating

6.5

Company rating: 6.5 out of 10

Based on 18 frontline employees who took The Breakroom Quiz


Job description


The sr data scientist is a member of the Data and Analytics Information Technology team, having a critical role in supporting the BHE (Berkshire Hathaway Energy) business transformations and asset performance management initiatives. The data scientist assists BHE business groups in translating business problems into technical and data requirements and participates in other Data and Analytics center of excellence initiatives. This position works with information technology teams across the company to extract, transform, validate and load data into data lake and analytics software systems including Microsoft Azure and Microsoft Power BI. This individual contributes to model development, model testing, production support, data validation and data integrity efforts in the course of daily work.
Provides Support to the Following Positions
Enterprise Analytics (IT), Berkshire Hathaway Energy,
Asset Performance and Investment Management, Berkshire Hathaway Energy,
Data Analytics Center of Excellence, Berkshire Hathaway Energy
Responsibilities
  • Mentor and support a team of data scientists by providing technical guidance and thought leadership to ensure successful project execution, while fostering a collaborative and innovative team environment.
    Demonstrated experience in team leadership, project planning and management, and stakeholder engagement.
    Partner with Product Owners to lead end-to-end data science initiatives, from problem definition and data exploration through model development, validation, and deployment.
    Apply advanced statistical and machine learning techniques to analyze complex datasets, uncover meaningful patterns, and develop predictive and prescriptive models.
    Contribute to the company's data strategy by identifying opportunities to leverage data science to improve business processes, optimize operations, and drive revenue growth.
    Collaborate with stakeholders across the organization, including Performance Engineering, Asset Performance, and Transmission & Distribution, to understand data needs and deliver solutions aligned with business objectives.
    Stay current with advancements in data science, machine learning, and GenAI technologies; evaluate and help implement new tools, algorithms, and frameworks to enhance team capabilities.
    Ensure high-quality documentation practices, including narrative documentation in wikis and technical documentation in version-controlled repositories.
    Operate within an agile environment that supports iterative development and continuous delivery.
    Communicate effectively with both technical and non-technical audiences across multiple platforms, including meetings, chat, email, and video conferencing.
  • Effectively communicate with colleagues on both technical and non-technical topics, across a variety of communications platforms, including voice and video calls, chat, and email.

Qualifications
Bachelor's degree in computer science, mathematics, software engineering or a related technical field. Master's in data science or related technical field preferred.
Eight or more years of experience in data science, with a proven track record of leading and delivering successful data science projects
Demonstrated hands-on experience designing and building data science proof-of-concepts and working with data from multiple sources, including relational databases, APIs, and modern data platforms (e.g., Delta tables), using SQL, PySpark, and Python.
Advanced proficiency in Python and/or R, with extensive experience using standard libraries for data wrangling (e.g., pandas, tidyverse), modeling (e.g., scikit-learn, caret, tidymodels), and data visualization (e.g., seaborn, ggplot2).
Established expertise in at least one core data science domain, such as supervised learning, time-series analysis, or survival modeling, with the ability to apply these techniques to complex, real-world business problems.
Proven experience productionizing advanced analytics and machine learning models, including transitioning solutions from development to operational and production environments.
Strong practical foundation in descriptive and inferential statistics, including hypothesis testing, confidence intervals, correlation analysis, and related statistical methods.
Hands-on experience with enterprise data visualization tools (Power BI preferred) and at least one cloud-based data platform (Azure and Databricks preferred).
Excellent verbal and written communication skills, with the ability to clearly communicate technical concepts, analytical results, and recommendations to both technical and non-technical stakeholders across multiple levels of the organization.
Strong leadership and interpersonal skills, with the ability to work independently, collaborate effectively within a team, and influence outcomes without direct authority.
Demonstrated initiative and resourcefulness, with the ability to navigate ambiguity, prioritize work effectively, and deliver results with limited guidance in a fast-changing environment.
Experience working in cross-functional team environments, partnering with engineering, product, and business stakeholders to deliver data-driven solutions.
Preferred experience with Spark, Azure DevOps, and MLOps practices.
About Us
MidAmerican Energy Company, a Midwest utility, provides regulated electric and natural gas service to more than 1.6 million customers in Illinois, Iowa, Nebraska and South Dakota. The company owns and operates a portfolio of power-generating assets, approximately 61% of which is wind generation.
About the Team
MidAmerican Energy Company is proud to be an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion or religious creed, age, national origin, ancestry, citizenship status (except as required by law), gender (including gender identity and expression), sex (including pregnancy), sexual orientation, genetic information, physical or mental disability, veteran or military status, familial or parental status, marital status or any other category protected by applicable local, state or U.S. federal law. Employees must be able to perform the essential functions of the position, with or without an accommodation.

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