Environmental Data Scientist Jobs in Minnesota (NOW HIRING)

We are seeking an experienced Senior Data Scientist to join a collaborative, fast-paced data science team focused on delivering measurable business value through machine learning. This is a hands-on technical role for someone who enjoys solving complex business problems, mentoring teammates, and driving continuous improvement across an established portfolio of production models.

Unlike organizations focused primarily on research or greenfield development, our team spends the majority of its time optimizing, enhancing, and scaling existing machine learning solutions. You''''''''ll partner closely with data engineers, business stakeholders, and fellow data scientists to improve model performance, identify new opportunities, and help shape the future direction of our data science practice.

The team operates in an agile, CI/CD environment with bi-weekly releases, making collaboration, iterative delivery, and continuous improvement essential to success.

• Lead the enhancement, tuning, retraining, and optimization of production machine learning models
• Design and implement advanced modeling solutions to solve complex business challenges
• Evaluate and recommend appropriate machine learning algorithms and modeling techniques
• Monitor model performance and identify opportunities to improve accuracy, scalability, and business impact
• Partner closely with Data Engineers to support data pipelines, feature engineering, and model deployment
• Build and maintain datasets using SQL and Python
• Develop and maintain work within Jupyter Notebooks in a cloud-based environment
• Lead model lifecycle activities, including testing, validation, deployment, and ongoing monitoring
• Mentor junior and mid-level Data Scientists through technical guidance, code reviews, and collaborative problem solving
• Partner with business stakeholders to translate business objectives into scalable analytical solutions
• Communicate technical concepts, recommendations, and results clearly to both technical and executive audiences
• Contribute to improving team standards, best practices, and machine learning processes

• 5+ years of experience in Data Science, Machine Learning, or Applied Analytics
• Expert-level proficiency with Python and SQL
• Extensive experience working in Jupyter Notebooks, preferably in a cloud environment
• Proven experience developing, deploying, monitoring, and maintaining production machine learning models
• Strong understanding of:
  • Machine learning model selection and evaluation
  • Model monitoring, drift detection, and performance optimization
  • Development versus production environments
  • Data pipelines and feature engineering
  • Model lifecycle management
• Experience leading or mentoring other Data Scientists
• Strong problem-solving and analytical skills
• Ability to work independently while collaborating effectively across cross-functional teams
• Excellent verbal and written communication skills with both technical and non-technical audiences

• Experience with Snowflake
• Experience supporting customer-facing machine learning applications
• Experience with personalization or recommendation engines
• Experience with customer lifecycle modeling, including churn prediction, propensity modeling, customer lifetime value (CLV), and segmentation
• Experience working in CI/CD and agile software development environments
• Experience collaborating closely with Data Engineering, Product, and business stakeholders
• Experience helping establish technical standards or best practices for Data Science teams

• A hands-on technical leader who enjoys building alongside the team
• A collaborative mentor who helps elevate those around them
• A versatile Data Scientist with broad modeling experience across multiple problem domains rather than deep specialization in a single technique
• Someone who takes ownership, drives outcomes, and proactively identifies opportunities for improvement
• A practical, business-minded problem solver who balances technical excellence with delivering measurable value
• Comfortable working in a fast-paced, iterative environment with frequent releases and changing priorities
• A team player who enjoys wearing multiple hats and contributing wherever needed

• Hybrid work environment with approximately three days per week onsite
• Agile team operating in two-week sprints
• Highly collaborative culture with close partnership between Data Science, Data Engineering, and business stakeholders
• Continuous learning environment where contractors are treated as integral members of the team and encouraged to contribute ideas and influence technical direction