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Data Science Research Assistant Remote Jobs in Minnesota

Fully Remote (St. Paul, MN) Employment Type: Full-Time Experience Level: Advanced (10+ Years) Are ... Exposure to data science and machine learning platforms including Dataiku, SAS, or IBM Watson ...

New

Lead Research Engineer

Eagan, MN · On-site +1

$104K - $137K/yr

... remote teams. * Be an Agile Person:With a strong sense of urgency and a desire to work in a fast ... Familiarity with the Python data science stack through exposure to libraries such as Numpy, Scipy ...

Data Engineer - Remote

Minnetonka, MN · On-site +1

$116K - $140K/yr

The position operates as part of a collaborative data engineering team, working closely with fellow data engineers and data science & reporting partners to meet evolving data requirements. The scope ...

Data Engineer - Remote

Minnetonka, MN · On-site +1

$116K - $140K/yr

The position operates as part of a collaborative data engineering team, working closely with fellow data engineers and data science & reporting partners to meet evolving data requirements. The scope ...

Showing results 21-40

Data Science Research Assistant Remote information

What are the key skills and qualifications needed to thrive as a data science research assistant remote?

To thrive as a Data Science Research Assistant (Remote), a solid background in statistics, programming (Python or R), and data analysis, often supported by relevant coursework or a degree, is essential. Familiarity with data visualization tools (e.g., Tableau), databases (SQL), and platforms like Jupyter Notebook, as well as experience with machine learning libraries, is typically required. Strong problem-solving abilities, attention to detail, self-motivation, and effective remote communication skills make candidates stand out. These competencies are crucial for managing complex data tasks, collaborating with team members virtually, and delivering reliable analytical insights.

What are common challenges faced by remote data science research assistants, and how can they be addressed?

Remote Data Science Research Assistants often encounter challenges such as maintaining clear communication with team members, managing time across different projects, and accessing necessary datasets or computing resources. Overcoming these hurdles typically involves leveraging collaboration tools like Slack or Zoom for regular check-ins, setting clear expectations with supervisors on deliverables, and ensuring secure, remote access to data and software. Proactively seeking feedback and participating in virtual team meetings can help foster a sense of connection and keep projects on track.

What is the difference between Data Science Research Assistant Remote vs Data Analyst Remote?

AspectData Science Research Assistant RemoteData Analyst Remote
Required CredentialsBachelor's or Master's in Data Science, Statistics, or related fieldBachelor's or Master's in Data Analysis, Statistics, or related field
Work EnvironmentRemote research projects, academic or research institutionsRemote data interpretation and reporting for various industries
Employer & Industry UsageUniversities, research labs, tech companiesBusiness, finance, healthcare, marketing

While both roles involve working with data remotely, Data Science Research Assistants focus on research projects, often in academic or research settings, requiring a strong foundation in data science and statistics. Data Analysts typically analyze and interpret data for business insights across various industries. The roles share similar credentials but differ in their primary focus and work environment.

What is a data science research assistant remote?

A Data Science Research Assistant (Remote) is a professional who supports data scientists and research teams by collecting, cleaning, analyzing, and visualizing data, often from a remote location. Their responsibilities may include assisting with experiment design, performing statistical analyses, preparing datasets, creating reports, and helping to develop or test machine learning models. Working remotely, they utilize collaboration tools and cloud platforms to work efficiently with distributed teams. This role is ideal for individuals with strong analytical skills, programming knowledge (such as Python or R), and an interest in research and data-driven problem solving.

What are popular job titles related to Data Science Research Assistant Remote jobs in Minnesota?

For Data Science Research Assistant Remote jobs in Minnesota, the most frequently searched job titles are:

What cities in Minnesota are hiring for Data Science Research Assistant Remote jobs?

Cities in Minnesota with the most Data Science Research Assistant Remote job openings:

Infographic showing various Data Science Research Assistant Remote job openings in Minnesota as of August 2026, with employment types broken down into 100% Full Time. Highlights an 100% Remote job distribution.

Director Data Science - Remote or Hybrid in MN or DC

UnitedHealth Group

Eden Prairie, MN • On-site, Remote

Full-time

Retirement

Re-posted 5 days ago


UnitedHealth Group rating

7.6

Company rating: 7.6 out of 10

Based on 146 frontline employees who took The Breakroom Quiz

189th of 887 rated healthcare providers


Job description

Optum Tech is a global leader in health care innovation. Our teams develop cutting-edge solutions that help people live healthier lives and help make the health system work better for everyone. From advanced data analytics and AI to cybersecurity, we use innovative approaches to solve some of health care's most complex challenges. Your contributions here have the potential to change lives. Ready to build the next breakthrough? Join us to start Caring. Connecting. Growing together.


You'll enjoy the flexibility to work remotely * from anywhere within the U.S. as you take on some tough challenges.    For all hires in the Minneapolis or Washington, D.C. area, you will be required to work in the office a minimum of four days per week.
Primary Responsibilities:

  • Define enterprise data science strategy: Own and drive the technical strategy for applied machine learning, Generative AI, Agentic AI, and advanced analytics across multiple domains and healthcare use cases
  • Lead development of advanced ML, GenAI, and agentic solutions: Provide hands-on technical direction for the design, development, and deployment of machine learning, deep learning, time-series, survival analysis, large language model (LLM), and agent-based AI systems in production environments
  • Establish modeling standards and best practices: Define and standardize modeling frameworks, feature engineering approaches, prompt and context engineering practices, evaluation methodologies, and validation standards across data science teams
  • Architect scalable ML and GenAI systems: Guide the design of production-grade ML and LLM systems including data pipelines, feature stores, retrieval-augmented generation (RAG), model serving infrastructure, agent orchestration frameworks, monitoring, and retraining workflows
  • Ensure responsible and reliable AI deployment: Implement consistent practices for model interpretability, explainability, bias assessment, fairness evaluation, guardrails, human oversight, and lifecycle management across deployed predictive, generative, and agentic AI systems
  • Oversee experimentation and performance monitoring: Define experimentation, benchmarking, and monitoring strategies including drift detection, recalibration, LLM evaluation, hallucination and safety checks, tool-use reliability, and performance management
  • Provide technical leadership and mentorship: Mentor principal and senior data scientists, review technical designs and modeling decisions, and provide guidance for complex analytical, GenAI, and agentic AI challenges
  • Influence cross-functional AI delivery: Partner with engineering, data, security, product, and platform teams to align data science solutions with enterprise platforms, infrastructure, reliability requirements, AI governance expectations, and executive priorities
  • Partner with payment integrity, clinical, claims, compliance, legal, product, and operations stakeholders to translate business problems such as overpayment detection, coding validation, policy adherence, aberrant billing patterns, and prepay/postpay review into scalable AI and analytics solutions
     

You'll be rewarded and recognized for your performance in an environment that will challenge you and give you clear direction on what it takes to succeed in your role as well as provide development for other roles you may be interested in. 
 

Required Qualifications:

  • 12 years of experience in data science, machine learning, or advanced analytics with 8 years developing and deploying production ML models
  • 8 years of experience using Python-based data science ecosystems (for example Pandas, NumPy, scikit-learn, PyTorch, or equivalent) and advanced SQL for large-scale analytics, experimentation, and data transformation
  • 7 years of experience in senior data science or technical leadership roles influencing modeling approaches, reviewing analytical work across teams, setting standards for model development and validation, and translating complex technical tradeoffs for senior stakeholders
  • 6 years of experience designing, deploying, or supporting production ML systems, including model serving, monitoring, retraining workflows, experimentation frameworks, ML lifecycle management, and evaluation of LLM or GenAI applications
  • 6 years of experience working with healthcare data such as claims, EHR, pharmacy, or laboratory datasets, including familiarity with healthcare coding systems such as ICD, CPT, NDC, SNOMED, and LOINC, as well as data interoperability standards including FHIR or HL7
  • 3 years of experience designing, building, or operationalizing Generative AI or LLM-based systems
  • 3 years of experience applying data science, machine learning, advanced analytics, or AI techniques to healthcare program integrity, payment integrity, fraud, waste, and abuse, claims payment accuracy, improper payment reduction, coding validation, provider behavior analytics, or related healthcare financial integrity use cases
  • 1 years of experience with Agentic AI concepts and implementations such as AI agents, agentic skills, model context protocols (MCPs), agent-to-agent (A2A) patterns, tool use, orchestration frameworks, or autonomous workflow execution
    *All employees working remotely will be required to adhere to UnitedHealth Group's Telecommuter Policy.


Pay is based on several factors including but not limited to local labor markets, education, work experience, certifications, etc. In addition to your salary, we offer benefits such as, a comprehensive benefits package, incentive and recognition programs, equity stock purchase and 401k contribution (all benefits are subject to eligibility requirements). No matter where or when you begin a career with us, you'll find a far-reaching choice of benefits and incentives. The salary for this role will range from $134,600 - $230,800 annually based on full-time employment. We comply with all minimum wage laws as applicable.

Application Deadline: This will be posted for a minimum of 2 business days or until a sufficient candidate pool has been collected. Job posting may come down early due to volume of applicants.
 

At UnitedHealth Group, our mission is to help people live healthier lives and make the health system work better for everyone. We believe everyone-of every race, gender, sexuality, age, location and income-deserves the opportunity to live their healthiest life. Today, however, there are still far too many barriers to good health which are disproportionately experienced by people of color, historically marginalized groups and those with lower incomes. We are committed to mitigating our impact on the environment and enabling and delivering equitable care that addresses health disparities and improves health outcomes - an enterprise priority reflected in our mission.


UnitedHealth Group is an Equal Employment Opportunity employer under applicable law and qualified applicants will receive consideration for employment without regard to race, national origin, religion, age, color, sex, sexual orientation, gender identity, disability, or protected veteran status, or any other characteristic protected by local, state, or federal laws, rules, or regulations.


UnitedHealth Group is a drug - free workplace. Candidates are required to pass a drug test before beginning employment. 


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