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Python Data Science Jobs in Minneapolis, MN (NOW HIRING)

BC Forward

Data Engineer: IV (Lead)

Minneapolis, MN · On-site

$119K - $143K/yr

Develop governed data products that support reporting, analytics, data science, automation, and business decision-making. Build and maintain data models and transformations using SQL, Python, dbt ...

New

Jostens

Data Scientist

Minneapolis, MN · On-site +1

This role is a member of the Data Science team, which is focused on developing advanced analytic ... in Python and/or R (both preferred) and SQL, experience using big data (billion+ rows) across ...

Mortenson

Data Analyst - Finance

Minneapolis, MN · On-site

Required : • Bachelor's degree in Data Science, Economics, Mathematics, Statistics, Analytics, or ... Python, or R • Experience with analytics and visualization tools, including those noted earlier ...

Cargill

Senior Data Scientist

Wayzata, MN · On-site

$148K/yr

... manage data science initiatives in food supply chain planning, including scoping, development ... Using Python and R prototyping languages and Java programming language. * Creating data performance ...

Cargill

Senior Data Scientist

Wayzata, MN

$148K/yr

... manage data science initiatives in food supply chain planning, including scoping, development ... Using Python and R prototyping languages and Java programming language. * Creating data performance ...

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All JobsPython Data Science JobsPython Data Science Jobs in Minneapolis, MN

Python Data Science information

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$61

$90

How much do python data science jobs pay per hour?

As of Jul 1, 2026, the average hourly pay for python data science in Minneapolis, MN is $61.19, according to ZipRecruiter salary data. Most workers in this role earn between $50.43 and $69.52 per hour, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive in the Python Data Science position, and why are they important?

To thrive in Python Data Science, you need strong programming skills in Python, a solid understanding of statistics, data manipulation, and experience with data analytics or machine learning, often supported by a bachelor’s or master’s degree in a quantitative field. Familiarity with tools such as pandas, NumPy, scikit-learn, Jupyter Notebooks, and knowledge of SQL are typically essential; certifications like Google Data Analytics or IBM Data Science can be advantageous. Critical thinking, problem-solving, and effective communication are key soft skills for translating data insights into actionable business recommendations. These skills are crucial to efficiently analyze large datasets, build predictive models, and deliver meaningful insights that drive decision-making.

How much does a Python data scientist make?

A Python data scientist's salary typically ranges from $80,000 to $130,000 annually, depending on experience, location, and industry. Professionals with strong skills in machine learning, statistical analysis, and data visualization tools like Pandas and TensorFlow tend to earn higher salaries.

What are typical day-to-day responsibilities in a Python Data Science role?

In a Python Data Science role, your typical day might involve collecting, cleaning, and preparing raw data, exploring datasets to uncover patterns and trends, and building or evaluating predictive models. You’ll regularly use Python libraries to conduct analyses, visualize results, and collaborate with cross-functional teams such as product managers or engineers to define business objectives. Presenting your findings in clear, actionable formats for both technical and non-technical stakeholders is also a key part of the job. This dynamic environment emphasizes continuous learning, problem-solving, and close communication with other departments to align analytical insights with organizational goals.

Is Python useful in data science?

Python is a fundamental tool for data scientists, including those in data science roles, due to its extensive libraries such as Pandas, NumPy, and scikit-learn that facilitate data analysis, visualization, and machine learning. Its simplicity and versatility make it a preferred programming language in the data science field, often complemented by knowledge of SQL and data visualization tools.

What is a Python Data Science job?

A Python Data Science job involves using Python to analyze, process, and visualize data to extract insights and inform decision-making. It typically includes working with libraries like Pandas, NumPy, and Scikit-learn for data manipulation, statistical analysis, and machine learning. Professionals in this role may clean and preprocess data, build models, and communicate findings through reports or visualizations. Python Data Scientists often work in industries like finance, healthcare, and technology to solve complex problems and optimize business strategies.

Is 40 too late for data science?

Age is not a barrier to becoming a data scientist; many professionals transition into data science at various ages. Success depends on acquiring relevant skills such as programming in Python, understanding statistics, and working with tools like Jupyter notebooks, regardless of age.

Is Python a high paying job?

Python Data Science roles are generally well-paid due to high demand for skills in data analysis, machine learning, and automation. Salaries vary based on experience, location, and industry, but professionals with Python expertise often earn above average wages in the tech sector.
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Python Data Science jobs near you
Infographic showing various Python Data Science job openings in Minneapolis, MN as of June 2026, with employment types broken down into 3% As Needed, 52% Full Time, 41% Part Time, 2% Contract, and 2% Nights. Highlights an 82% Physical, 5% Hybrid, and 13% Remote job distribution, with an average salary of $127,273 per year, or $61.2 per hour.
Data Scientist (AI, Big Data, SQL, Python) - W2 Only - REMOTE

Data Scientist (AI, Big Data, SQL, Python) - W2 Only - REMOTE

Resource Point LLC

Minneapolis, MN • Remote

Contractor

Posted 8 days ago

Job description

Job Title: Data Scientist (AI, Big Data, SQL, Python) - W2 Only - REMOTE

Location: Minneapolis, MN

Duration: 12+ Months

Description:
Our audit and governance functions require a centralized data leader who can:

  • Architect scalable, secure, compliant data pipelines
  • Translate complex datasets into actionable insights for regulatory and operational decisions
  • Build intuitive, low-maintenance tools that empower non-technical users across the PA experience

Responsibilities:

  • Data Collection & Cleaning - They gather data from various sources and clean it to ensure it's usable—removing errors, filling in missing values, and standardizing formats.
  • Exploratory Data Analysis (EDA) - They explore the data to understand patterns, trends, and relationships using statistical techniques and visualizations.
  • Model Building - They build predictive models using machine learning algorithms to forecast outcomes or classify data.
  • Interpretation & Communication - They translate complex results into actionable insights and communicate them to stakeholders through reports, dashboards, or presentations.
  • Deployment & Monitoring - In some cases, they help deploy models into production systems and monitor their performance over time.

Ideal Background: 

  • Healthcare specific background would be helpful.
  • But candidate must be experienced in elements of statistics, computer science, and domain expertise to help organizations make data-driven decisions.
  • As well as, build and maintain artificial intelligence (AI) driven platforms/solutions.

Required Skills:

  • Programming: Python, R, SQL
  • Statistics & Mathematics
  • Machine Learning & AI
  • Data Visualization: Tools like Tableau, Power BI, or libraries like Matplotlib and Seaborn
  • Big Data Tools: Spark, Hadoop (for large-scale data)

Preferred:

  • Advanced SQL and Python for analytics, ETL, and automation
  • Data modeling, warehousing, and pipeline orchestration (cloud, native stack)
  • Dashboarding (Power BI; Streamlit or similar) and reproducible analytics (versioning, CI/CD preferred)
  • Healthcare data familiarity (claims, PA & appeals, pharmacy) and regulatory contexts (CMS, NCQA, URAC, ERISA, state rules)
  • Data security, privacy, and compliance best practices.