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Manager Environmental Data Scientist Jobs (NOW HIRING)

... environments • Advanced programming proficiency in Python or R with strong expertise in machine ... to data science workflows • Data visualization and communication excellence with ability to ...

... environments. • Implement batch and real-time model scoring. • Assemble large, complex data ... and management of applications, infrastructure and connectivity. We are one of the leading ...

Data Scientist General Information Requisition # 674 Locations USA-VA-Arlington Posting Date 03/04 ... data environments. You will work directly with government clients, program managers, and technical ...

... into data-rich environments to find pieces of information puzzle. • Develop algorithms and ... management • Experience analyzing large-scale datasets • Experience with data gathering ...

However, the biggest challenge during managing this data comes across in the terms of 'Value Realization'. The true measure of success is to be able to put the data science insights into actionable ...

... environments to find the pieces of their information puzzle. You'll guide and mentor your team as ... Experience with data management in SQL, including managing large-scale data across multiple ...

This Data Science role will report to our Data Science manager who leads Data Science for Amplify ... Experience deploying machine learning models from research environments into production ...

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Manager Environmental Data Scientist information

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$37.5K

$122.7K

$196.5K

How much do manager environmental data scientist jobs pay per year?

As of Jun 9, 2026, the average yearly pay for manager environmental data scientist in the United States is $122,738.00, according to ZipRecruiter salary data. Most workers in this role earn between $98,500.00 and $136,000.00 per year, depending on experience, location, and employer.

What is the difference between Manager Environmental Data Scientist vs Environmental Data Scientist?

AspectManager Environmental Data ScientistEnvironmental Data Scientist
CredentialsBachelor's or Master's in Environmental Science, Data Science, or related fields; often requires leadership experienceBachelor's or Master's in Environmental Science, Data Science, or related fields; technical expertise in data analysis
Work EnvironmentLeads teams, manages projects, collaborates with stakeholdersPerforms data analysis, develops models, supports environmental research
Employer & Industry UsageUsed in organizations with environmental departments, government agencies, consulting firmsCommon in research institutions, environmental agencies, private sector companies

The main difference is that the Manager Environmental Data Scientist oversees teams and projects, focusing on leadership and strategic planning, while the Environmental Data Scientist primarily conducts data analysis and modeling to support environmental initiatives.

What cities are hiring for Manager Environmental Data Scientist jobs? Cities with the most Manager Environmental Data Scientist job openings:
What are the most commonly searched types of Environmental Data Scientist jobs? The most popular types of Environmental Data Scientist jobs are:
What states have the most Manager Environmental Data Scientist jobs? States with the most job openings for Manager Environmental Data Scientist jobs include:

Full-time

Posted 6 days ago


Job description

Job Summary:
NTT DATA North America is a trusted global innovator of business and technology services. They are currently seeking a Data Scientist to play a pivotal role in planning, executing, and delivering machine learning-based projects that drive business impact.
Responsibilities:
• Collect, clean, and analyze datasets from diverse internal and external sources, applying advanced data wrangling techniques to handle structured, semi-structured, and unstructured data while ensuring completeness, consistency, and accuracy.
• Acquire access to various databases and source systems (SQL, NoSQL, graph databases) and create data pipelines for efficient and repeatable data science projects.
• Apply statistical analysis and visualization techniques (hierarchical clustering, principal components analysis (PCA)) to explore and prepare data.
• Design, develop, and validate machine learning, statistical, and optimization models for classification, regression, clustering, recommendation, and prediction tasks.
• Select appropriate algorithms and models for AI /ML, and rigorously test them for accuracy, robustness, and fairness.
• Perform feature selection and engineering, create predictive variables, and experiment with transformations to enhance performance and interpretability.
• Integrate domain knowledge into ML solutions (e.g., care delivery, financial risk, customer journey, quality prediction, sales, marketing).
• Conduct controlled experiments (A/B and multivariate testing), to evaluate hypotheses, measure workflow changes, and quantify the impact of AI solutions on operations.
• Collaborate with MLOps, data engineers, and IT to evaluate deployment options, and establish best practices around ML production infrastructure.
• Continuously monitor execution and health of production ML models, recalibrating as needed and updating them to reflect new data or changing business conditions.
• Work with cross-functional teams, collaborating with stakeholders to refine objectives, and ensure alignment between technical outputs and strategic goals.
• Create dashboards, and interactive visualizations that communicate results to a wide range of audiences, turning technical findings into actionable recommendations.
• Communicate complex projects, models, and results to diverse audiences, including executives and frontline staff, using storytelling and presentation techniques.
• Stay current with industry research and emerging technologies in AI, machine learning, and optimization, proactively experimenting with new methods and recommending adoption of tools that strengthen analytics capabilities.
• Mentor junior data scientists and analysts, provide guidance on technical approaches and model interpretation, and promote collaboration across teams.
Qualifications:
Required:
• Education: Master's, or PhD in Computer Science, Data Science, Engineering, Statistics, Applied Mathematics, Operations Research, or a related quantitative field.
• 3-5 years of hands-on experience planning and executing end-to-end data science projects with demonstrated impact on clinical or operational outcomes in business environments
• Advanced programming proficiency in Python or R with strong expertise in machine learning frameworks (scikit-learn, TensorFlow, PyTorch) and statistical analysis tools
• Expertise in machine learning and statistical techniques including supervised/unsupervised learning, deep learning, NLP, computer vision, regression models, ensemble methods, and experimental design (A/B testing)
• Strong data engineering capabilities including SQL/NoSQL database programming, distributed computing tools (Hadoop, Spark, Kafka), data pipeline development, and experience with cloud platforms (AWS, Azure, GCP)
• Production ML and MLOps experience including model deployment, monitoring, containerization (Docker, Kubernetes), version control, and applying DevOps principles to data science workflows
• Data visualization and communication excellence with ability to create compelling dashboards (Tableau, Power BI), translate complex technical findings into actionable insights, and present to diverse audiences from executives to frontline staff
• Cross-functional collaboration skills with proven ability to work in agile environments, partner with stakeholders to align technical solutions with business objectives, and mentor junior team members
Preferred:
• Specialization in ML, AI, cognitive science, or data science is highly preferred.
• Healthcare domain knowledge preferred, particularly experience with Epic EHR systems, clinical workflows, and healthcare data standards, along with relevant certifications (Clarity /Caboodle, Google Cloud ML Engineer, AWS ML Specialist)
Company:
NTT DATA, Inc. is a trusted global innovator of business and technology services. Founded in 1988, the company is headquartered in Plano, USA, with a team of 10001+ employees. The company is currently Late Stage.