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Data Science Teaching Assistant Jobs in Utah (NOW HIRING)

L3HHCM20

Lead, Data Architect

Salt Lake City, UT

Solution Mapping and Design * Assist in evaluating and identifying appropriate data, analytics, and ... Work closely with data scientists, data engineers, and IT partners to ensure successful ...

Omnicom

Data Analyst

Salt Lake City, UT · On-site

Data Science and Analytics MRM is part of the Omnicom Precision Marketing activation practice. This ... in a timely fashion. - Assist in website analytics initiatives, including website tag ...

Omnicom

Data Analyst

Salt Lake City, UT · Hybrid

Data Science and Analytics MRM is part of theOmnicomPrecision Marketing activation practice.This ... in a timely fashion. - Assist in website analytics initiatives, including website tag ...

Upbound

Data Engineer IV - AI & Data Products

Draper, UT · On-site

$107K - $128K/yr

  • Medical

  • Dental

  • Vision

  • Retirement

... Scientists and AI use cases/agents. * Create and maintain data domain assets (pipelines/ETLs ... Perform data analysis required to troubleshoot data related issues and assist in the resolution of ...

Atonom

Data Engineer

Provo, UT

$109K - $131K/yr

  • Medical

  • PTO

Recent graduate (or soon to be) in Computer Science, Data Science, Data Engineering, Software Engineering, or a related technical field. Self-taught candidates with relevant projects are also welcome.

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All JobsData Science Teaching JobsData Science Teaching Assistant JobsData Science Teaching Assistant Jobs in Utah

Data Science Teaching Assistant information

What is a data science teaching assistant?

Data Science Teaching Assistants (TAs) support instructors and students in data science courses or bootcamps. They help clarify complex concepts, assist with coding exercises, answer student questions, and sometimes grade assignments or provide feedback. TAs often have a strong foundation in programming, statistics, and data analysis, and they play a key role in enhancing the learning experience. Their involvement can range from leading small group sessions to providing one-on-one help during office hours.

What skills and qualifications are needed to be a data science teaching assistant?

To thrive as a Data Science Teaching Assistant, you need a solid understanding of data science concepts, programming (especially Python or R), statistics, and often a relevant degree or coursework. Familiarity with tools such as Jupyter Notebooks, data visualization libraries, and version control systems like Git is typically required. Strong communication, patience, and the ability to explain complex topics clearly are standout soft skills in this role. These skills enable effective student support, reinforce learning outcomes, and contribute to a positive educational environment.

What challenges do data science teaching assistants face when supporting student learning, and how can they be addressed?

Data Science Teaching Assistants often encounter challenges such as explaining complex concepts in accessible ways, managing diverse student skill levels, and providing timely feedback on assignments. To address these challenges, it's important to use clear examples, encourage open communication, and adapt explanations to different learning styles. Collaborating closely with course instructors and leveraging office hours or online discussion forums can also help TAs support students more effectively and ensure no one falls behind.

What is the difference between Data Science Teaching Assistant vs Data Analyst?

AspectData Science Teaching AssistantData Analyst
Required CredentialsOften a degree in data science, statistics, or related field; familiarity with data toolsDegree in statistics, data analysis, or related field; proficiency in data tools
Work EnvironmentEducational settings, labs, online coursesBusiness, corporate, or research environments
Employer & Industry UsageUniversities, online education platformsCorporations, consulting firms, government agencies
Common Search & Comparison IntentUnderstanding teaching roles in data science educationUnderstanding data analysis tasks and roles

While both roles involve working with data and require similar technical skills, a Data Science Teaching Assistant primarily supports educational activities, assisting instructors and students in learning data science concepts. In contrast, a Data Analyst focuses on analyzing data to generate insights for business decisions. The roles differ mainly in their work environment and primary objectives, though they share foundational data skills.

How to become a data science teaching assistant?

To become a data science teaching assistant, candidates typically need a strong background in data science, statistics, or related fields, along with proficiency in programming languages like Python or R. Relevant experience with data analysis, machine learning, and teaching or mentoring skills are also important, and some positions may require a graduate degree or teaching experience. Gaining familiarity with tools such as Jupyter notebooks and SQL can enhance qualifications.

What are popular job titles related to Data Science Teaching Assistant jobs in Utah?

For Data Science Teaching Assistant jobs in Utah, the most frequently searched job titles are:

What job categories do people searching Data Science Teaching Assistant jobs in Utah look for?

The top searched job categories for Data Science Teaching Assistant jobs in Utah are:

What cities in Utah are hiring for Data Science Teaching Assistant jobs?

Cities in Utah with the most Data Science Teaching Assistant job openings:

Data Science Teaching Assistant jobs near you

Lead, Data Architect

L3HHCM20

Salt Lake City, UT

Full-time

Posted 27 days ago

Job description

Job Title: Lead, Data Architect
Job Code: 41482
Job Location: Salt Lake City, Utah
Job Schedule: 9/80 (employee's work 9 out of every 14 days - totaling 80 hours worked - and have every other Friday off)

POSITION DESCRIPTION

As a member of the Data Analytics team, you will support efforts to solve important business problems, help internal departments make informed decisions, and deliver analytical insights to the business. We are seeking an experienced Data Architect / AI/ML Solutions professional who can work closely with business stakeholders and technical teams to translate business needs into effective data and analytics solutions.

The ideal candidate will have experience supporting analytics, AI/ML, and data solution initiatives, with strong skills in stakeholder communication, requirements gathering, solution development, and cross-functional collaboration. This individual will be responsible for understanding business requirements, identifying opportunities to apply data, analytics, and AI/ML technologies, and contributing to the design and implementation of solutions that support business objectives.

ESSENTIAL FUNCTIONS

  • Business Requirement Gathering
    • Engage with stakeholders across business functions to gather, understand, and document analytical and data-related needs.
    • Participate in workshops and meetings to help translate business requirements into functional and technical specifications.
  • Solution Mapping and Design
    • Assist in evaluating and identifying appropriate data, analytics, and AI/ML approaches that align with business requirements.
    • Contribute to the design of scalable and practical analytics solutions that support business goals.
  • Collaboration and Communication
    • Work closely with data scientists, data engineers, and IT partners to ensure successful implementation of AI/ML solutions.
    • Communicate technical concepts, solution options, and expected outcomes to both technical and non-technical stakeholders.
  • Project Management:
    • Manage project timelines, resources, and deliverables to ensure the timely and successful deployment of AI/ML solutions.
    • Provide regular updates to stakeholders and ensure alignment throughout the project lifecycle.
  • Quality and Validation
    • Participate in validation, testing, and review of analytics and AI/ML solutions to ensure they meet business and performance requirements.
    • Support data quality, consistency, and governance practices within assigned solutions.
  • Technology Awareness
    • Stay informed about the latest developments in AI/ML technologies and industry best practices.
    • Assess new tools and technologies for potential application within the organization.

REQUIRED QUALIFICATIONS (ONE OF THE FOLLOWING)

  • Bachelor's degree in Data Science, Computer Science, Mathematics, Engineering, or related field and minimum 9 years of prior relevant experience.
  • Graduate Degree and a minimum of 7 years of prior related experience.
  • In lieu of a degree, minimum of 13 years of prior related experience.

 

PREFERRED ADDITIONAL SKILLS

         Experience in data science, advanced analytics, data analysis, or related role.

         Strong knowledge of AI/ML concepts and common models such as regression, classification, clustering, and deep learning.

         Proven experience in requirement gathering, solution design, and project management.

         Strong communication skills with the ability to convey technical concepts to non-technical audiences.

         Strong problem-solving skills and a proactive approach to identifying and addressing business challenges.

         Experience with programming languages and tools commonly used in AI/ML workflows (e.g., Python, R, SQL).

         Coding experience with C#, Visual Basic .NET, JavaScript, XML, HTML, CSS is a plus, but not required.

         Knowledge of ANSI SQL and data manipulation/query development.

         Experience with cloud-based AI/ML platforms (e.g., AWS bedrock, Azure Foundry, etc.).

         Open-source AI/ML solutions (various)

         Experience with Agile software development (i.e., Jira and Confluence)

         Experience in government, defense, or a related regulated sector

         Relevant certifications in data, analytics, cloud, or project delivery are beneficial.

 

WORK ENVIRONMENT

         Collaborative and dynamic environment with opportunities for professional growth.

         Occasional travel to meet with business stakeholders or attend industry events.

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