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Temporary Data Scientist Machine Learning Jobs in Minneapolis, MN

On-Demand Group

Sr. Data Scientist

Minneapolis, MN · On-site

Evaluate and recommend appropriate machine learning algorithms and modeling techniques * Monitor ... Mentor junior and mid-level Data Scientists through technical guidance, code reviews, and ...

Jobtailor

Senior Data Scientist

Minneapolis, MN · On-site

$120 - $180/hr

Expertise in data science, machine learning, data mining, operations research, and statistical modeling techniques, specifically for high-volume and complex datasets. * Knowledge of best coding ...

System One

Data Scientist

Minneapolis, MN · On-site

System One is seeking a Data Scientist with 5+ years of hands-on experience to design, build, and deploy production-grade machine learning models. In this role, you will bridge the gap between ...

System One

Data Scientist

Minneapolis, MN · On-site

System One is seeking a Data Scientist with 5+ years of hands-on experience to design, build, and deploy production-grade machine learning models. In this role, you will bridge the gap between ...

System One

Data Scientist

Minneapolis, MN · On-site

System One is seeking a Data Scientist with 5+ years of hands-on experience to design, build, and deploy production-grade machine learning models. In this role, you will bridge the gap between ...

System One

Data Scientist

Minneapolis, MN · On-site

System One is seeking a Data Scientist with 5+ years of hands-on experience to design, build, and deploy production-grade machine learning models. In this role, you will bridge the gap between ...

System One

Data Scientist

Minneapolis, MN · On-site

System One is seeking a Data Scientist with 5+ years of hands-on experience to design, build, and deploy production-grade machine learning models. In this role, you will bridge the gap between ...

Accelint

Data Scientist

Saint Paul, MN · On-site

$105K - $126K/yr

The Data Scientist will apply knowledge of statistics, machine learning, programming, and data modeling. They use a flexible, analytical approach to design, develop, and evaluate predictive models ...

Dlapiper

Data Scientist

Minneapolis, MN · On-site +1

A specialization in machine-learning, artificial intelligence, cognitive science or data science is preferred. Must be self-driven, curious and creative. * Experience must include creating and using ...

Accelint Holdings LLC

Data Scientist

Saint Paul, MN · On-site

$106 - $127/hr

The Data Scientist will apply knowledge of statistics, machine learning, programming, and data modeling. They use a flexible, analytical approach to design, develop, and evaluate predictive models ...

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All JobsData Scientist Machine Learning JobsTemporary Data Scientist Machine Learning JobsTemporary Data Scientist Machine Learning Jobs in Minneapolis, MN

Temporary Data Scientist Machine Learning information

See Minneapolis, MN salary details

$39.1K

$128.1K

$205.1K

How much do temporary data scientist machine learning jobs pay per year?

As of Aug 24, 2026, the average yearly pay for temporary data scientist machine learning in Minneapolis, MN is $128,114.00, according to ZipRecruiter salary data. Most workers in this role earn between $102,800.00 and $142,000.00 per year, depending on experience, location, and employer.

What does a temporary data scientist specializing in machine learning do?

A Temporary Data Scientist specializing in Machine Learning is responsible for designing, building, and deploying machine learning models to analyze data and generate insights, but works on a contract or short-term basis. Their duties often include data preprocessing, model selection and validation, and communicating results to stakeholders. They may also be tasked with automating processes, cleaning large datasets, and collaborating with other teams to implement solutions. The temporary nature of the job means they often focus on specific projects or provide support during peak periods.

What are the key skills and qualifications needed to thrive as a temporary data scientist specializing in machine learning?

To thrive as a Temporary Data Scientist Machine Learning, you generally need a strong background in statistics, programming (Python or R), and experience with machine learning algorithms, often supported by a degree in computer science, mathematics, or a related field. Familiarity with data visualization tools (like Tableau), machine learning libraries (such as scikit-learn, TensorFlow, or PyTorch), and version control systems (e.g., Git) is typically required. Strong problem-solving abilities, adaptability, and effective communication are crucial soft skills for collaborating with teams and translating technical findings to stakeholders. These skills ensure that temporary data scientists can quickly contribute actionable insights, drive data-driven decisions, and add value within a limited time frame.

What are some typical projects or tasks a temporary data scientist specializing in machine learning might work on?

As a temporary Data Scientist focusing on machine learning, you can expect to work on short-term, high-impact projects such as building predictive models, cleaning and preparing data, or developing automated analytics solutions. You may be brought in to support ongoing initiatives, provide expertise for a specific project phase, or help accelerate a backlog of tasks. Collaboration is common, and you'll likely work closely with data engineers, business analysts, and domain experts to understand requirements and deliver actionable insights within tight deadlines. This role offers exposure to diverse datasets and tools, and is an excellent opportunity to rapidly expand your experience and network.

What is the difference between Temporary Data Scientist Machine Learning vs Temporary Data Analyst?

AspectTemporary Data Scientist Machine LearningTemporary Data Analyst
Required CredentialsBachelor's/Master's in Data Science, Computer Science, or related fields; knowledge of ML algorithmsBachelor's in Statistics, Mathematics, or related fields; proficiency in data analysis tools
Work EnvironmentProject-based, collaborative teams, tech-focused companiesBusiness units, reporting teams, data-driven departments
Employer & Industry UsageTech firms, finance, healthcare, e-commerceRetail, marketing, finance, consulting

Temporary Data Scientist Machine Learning roles focus on developing and deploying machine learning models, requiring advanced analytics skills. Temporary Data Analysts primarily interpret data, generate reports, and support decision-making. While both roles involve data handling, Data Scientists with ML expertise work on predictive modeling, whereas Data Analysts focus on descriptive analytics. The choice depends on the project needs and skill requirements.

What are the most commonly searched types of Data Scientist Machine Learning jobs in Minneapolis, MN?

The most popular types of Data Scientist Machine Learning jobs in Minneapolis, MN are:

What are popular job titles related to Temporary Data Scientist Machine Learning jobs in Minneapolis, MN?

For Temporary Data Scientist Machine Learning jobs in Minneapolis, MN, the most frequently searched job titles are:

What job categories do people searching Temporary Data Scientist Machine Learning jobs in Minneapolis, MN look for?

The top searched job categories for Temporary Data Scientist Machine Learning jobs in Minneapolis, MN are:

Temporary Data Scientist Machine Learning jobs near you

Sr. Data Scientist

On-Demand Group

Minneapolis, MN • On-site

Other

Posted 11 days ago

Job description

About the Role

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.

Key Responsibilities
  • 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
Required Qualifications
  • 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
Preferred Qualifications
  • 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
What We''''''''re Looking For
  • 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
Work Environment
  • 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

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