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Data Science Assistant Jobs in Arizona (NOW HIRING)

These efforts will encompass exploring AI solutions that serve as intelligent work assistants ... Bachelor's degree in mathematics, computer science, statistics, economics, finance, actuarial ...

... capabilities that assist with data ingestion, feature engineering, data management, and ... Required : • Bachelor's degree in Computer Science, Statistics, Data Science, Information Systems ...

Partner with engineering and data science teams to embed AI assistance directly into data products ... Contribute to the design of agentic data systems, where autonomous or semi autonomous agents assist ...

Science Intern

Tucson, AZ

$14.25 - $19/hr

The science intern will carry out cutting-edge research on cold brown dwarfs ... The intern will assist in the reduction, analysis, and interpretation of recently acquired data ...

AI Data Engineer - Manager

Tempe, AZ · On-site

$109K - $131K/yr

You will manage day-to-day delivery with an onshore/offshore team, partnering with data science, ML ... Build tools and capabilities that assist with data ingestion, feature engineering, data management ...

Science Intern

Tucson, AZ

$14.25 - $19/hr

The science intern will carry out cutting-edge research on cold brown dwarfs ... The intern will assist in the reduction, analysis, and interpretation of recently acquired data ...

Science Intern

Tucson, AZ

$14.25 - $19/hr

The science intern will carry out cutting-edge research on cold brown dwarfs ... The intern will assist in the reduction, analysis, and interpretation of recently acquired data ...

Collaborate with technical SMEs (data engineers, data scientists, cloud architects) to validate ... * Assist sellers in leveraging partner incentives and funding (POCs, pilots, workshops ...

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Data Science Assistant information

What are Data Science Assistants?

Data Science Assistants are professionals who support data scientists and analytics teams by handling tasks such as data collection, data cleaning, preparing datasets, conducting preliminary analyses, and creating visualizations. They often work with large datasets, assist in maintaining data integrity, and help automate routine processes. Their role allows data scientists to focus on more complex modeling and analytical work, making the overall workflow more efficient. Data Science Assistants typically have a foundational understanding of statistics, programming (such as Python or R), and data management tools.

What are the key skills and qualifications needed to thrive as a Data Science Assistant, and why are they important?

To thrive as a Data Science Assistant, you need a solid understanding of statistics, data analysis, and programming (often with a background in mathematics, computer science, or a related field). Familiarity with tools like Python or R, data visualization software, and experience with databases or spreadsheet systems are typically required. Attention to detail, strong problem-solving abilities, and effective communication set outstanding candidates apart. These skills are crucial for supporting data-driven decision-making and ensuring accurate, actionable insights for organizations.

Is 40 too late for data science?

Data science assistants can enter the field at any age, as success depends on skills, experience, and continuous learning. Many professionals transition into data science later in their careers by acquiring relevant knowledge in programming, statistics, and tools like Python or R. Age is not a barrier if you develop the necessary competencies and stay current with industry trends.

How does a Data Science Assistant typically collaborate with data scientists and other team members on projects?

As a Data Science Assistant, you will frequently support data scientists by preparing datasets, conducting preliminary data analysis, and creating visualizations. You will often work closely with analysts, engineers, and subject matter experts to gather requirements and ensure data is cleaned and formatted appropriately. Collaboration is a key part of the role, as you may participate in team meetings, share findings, and help with documentation to keep projects running smoothly. This supportive environment provides an excellent opportunity to learn from experienced professionals and gain exposure to the full data science workflow.

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

AspectData Science AssistantData Analyst
Required CredentialsBachelor's in Data Science, Statistics, or related fieldBachelor's in Statistics, Mathematics, or related field
Work EnvironmentTech companies, research labs, data-driven departmentsBusiness, finance, marketing, healthcare sectors
Employer & Industry UsageUsed in data science teams for supporting models and analysisUsed across industries for interpreting data and generating reports

While both roles involve working with data, a Data Science Assistant typically supports data science projects, focusing on data preparation and model testing. A Data Analyst primarily interprets data to generate insights and reports. The roles overlap in skills and work environments but differ in their core responsibilities and focus areas.

What is a data scientist assistant?

A data scientist assistant supports data scientists by collecting, cleaning, and analyzing data, often using tools like Python or R. They help prepare reports, build models, and may need knowledge of statistics and data visualization to contribute effectively to data projects.

Is AI replacing data scientists?

AI is transforming the role of data scientists by automating routine tasks such as data cleaning and basic analysis, but it does not replace the need for skilled professionals to interpret results, develop models, and make strategic decisions. Data scientists are increasingly required to work alongside AI tools, focusing on complex problem-solving, model development, and domain expertise. Continuous learning and proficiency in programming languages like Python and tools such as machine learning frameworks remain essential for the role.

Which is better, DS or CS?

For a Data Science Assistant role, both Data Science (DS) and Computer Science (CS) provide valuable skills; DS focuses on data analysis, modeling, and visualization, while CS emphasizes algorithms, programming, and software development. The choice depends on the specific job requirements and your career goals, but familiarity with programming languages like Python or R and understanding of data tools are essential in both fields.
What are the most commonly searched types of Data Science jobs in Arizona? The most popular types of Data Science jobs in Arizona are:
What are popular job titles related to Data Science Assistant jobs in Arizona? For Data Science Assistant jobs in Arizona, the most frequently searched job titles are:
Infographic showing various Data Science Assistant job openings in Arizona as of June 2026, with employment types broken down into 1% As Needed, 85% Full Time, 12% Part Time, 1% Temporary, and 1% Contract. Highlights an 98% Physical, 1% Hybrid, and 1% Remote job distribution.
Data Scientist Lead

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Posted 27 days ago


USAA rating

8.3

Company rating: 8.3 out of 10

Based on 251 frontline employees who took The Breakroom Quiz

34th of 141 rated banks


Job description

Why USAA?

At USAA, our mission is to empower our members to achieve financial security through highly competitive products, exceptional service and trusted advice. We seek to be the #1 choice for the military community and their families.

Embrace a fulfilling career at USAA, where our core values – honesty, integrity, loyalty and service – define how we treat each other and our members. Be part of what truly makes us special and impactful.

We are proud to support active-duty military spouses. USAA roles may offer remote or hybrid flexibility for active-duty military spouses consistent with applicable policy and business needs.

The Opportunity

We are seeking a Lead AI Individual Contributor Data Scientist to drive the strategic adoption and practical application of AI, particularly Generative AI, across USAA Federal Savings Bank (USAA FSB). You will identify and implement AI solutions – whether cutting-edge industry advancements, existing solutions from other USAA lines of business, or tools that connect and integrate various platforms and IT systems – to boost productivity, automate processes, reduce costs, and create new revenue streams within the financial services context. This includes actively scouting for and evaluating external AI tools and technologies through various channels, such as professional networks and ties to academia, AI events, conferences, academic research, and a keen awareness of technological advancements, to ensure we are leveraging the most innovative solutions available. These efforts will encompass exploring AI solutions that serve as intelligent work assistants, streamlining and optimizing routine tasks for our employees, ultimately enhancing the experience and value delivered to our USAA members, while also considering the visionary potential and ethical implications of these advancements.

This role is remote eligible in the continental U.S. with occasional business travel. However, individuals residing within a 60-mile radius of a USAA office will be expected to work on-site four days per week.

Relocation assistance is available for this position.

What you'll do:
  • Gathers, interprets, and manipulates complex structured and unstructured data to enable advanced analytical solutions for the business.
  • Leads and conducts advanced analytics leveraging machine learning, simulation, and optimization to deliver business insights and achieve business objectives.
  • Guides team on selecting the appropriate modeling technique and/or technology with consideration to data limitations, application, and business needs.
  • Develops and deploys models within the Model Development Control (MDC) and Model Risk Management (MRM) framework.
  • Composes and peer reviews technical documents for knowledge persistence, risk management, and technical review audiences.
  • Partners with business leaders from across the organization to proactively identify business needs and proposes/recommends analytical and modeling projects to generate business value. Works with business and analytics leaders to prioritize analytics and highly complex modeling. problems/research efforts.
  • Leads efforts to build and maintain a robust library of reusable, production-quality algorithms and supporting code, to ensure model development and research efforts are transparent and based on the highest quality data.
  • Assists team with translating business request(s) into specific analytical questions, executing analysis and/or modeling, and communicating outcomes to non-technical business colleagues with a focus on business action and recommendations.
  • Manages project portfolio milestones, risks, and impediments. Anticipates potential issues that could limit project success or implementation and escalates as needed.
  • Establishes and maintains best practices for engaging with Data Engineering and IT to deploy production-ready analytical assets consistent with modeling best practices and model risk management standards.
  • Interacts with internal and external peers and management to maintain expertise and awareness of cutting-edge techniques. Actively seeks opportunities and materials to learn new techniques, technologies, and methodologies.
  • Serves as a mentor to data scientists in modeling, analytics, computer science, business acumen, and other interpersonal skills.
  • Participates in enterprise-level efforts to drive the maintenance and transformation of data science technologies and culture.
  • Ensures risks associated with business activities are effectively identified, measured, monitored, and controlled in accordance with risk and compliance policies and procedures.

What you have:

  • Bachelor’s degree in mathematics, computer science, statistics, economics, finance, actuarial sciences, science and engineering, or other similar quantitative discipline; OR 4 years of experience in statistics, mathematics, quantitative analytics, or related experience (in addition to the minimum years of experience required) may be substituted in lieu of degree.
  • 8 years of experience in a predictive analytics or data analysis OR Advanced Degree (e.g., Master’s, PhD) in mathematics, computer science, statistics, economics, finance, actuarial sciences, science and engineering, or other similar quantitative discipline and 6 years of experience in predictive analytics or data analysis.
  • 6 years of experience in training and validating statistical, physical, machine learning, and other advanced analytics models.
  • 4 years of experience in one or more dynamic scripted language (such as Python, R, etc.) for performing statistical analyses and/or building and scoring AI/ML models.
  • Expert ability to write code that is easy to follow, well documented, and commented where necessary to explain logic (high code transparency).
  • Strong experience in querying and preprocessing data from structured and/or unstructured databases using query languages such as SQL, HQL, NoSQL, etc.
  • Strong experience in working with structured, semi-structured, and unstructured data files such as delimited numeric data files, JSON/XML files, and/or text documents, images, etc.
  • Excellent demonstrated skill in performing ad-hoc analytics using descriptive, diagnostic, and inferential statistics.
  • Proven ability to assess and articulate regulatory implications and expectations of distinct modeling efforts.
  • Project management experience that demonstrates the ability to anticipate and appropriately manage project milestones, risks, and impediments. Demonstrated history of appropriately communicating potential issues that could limit project success or implementation.
  • Expert level experience with the concepts and technologies associated with classical supervised modeling for prediction such as linear/logistic models, discriminant analysis, support vector machines, decision trees, forest models, etc.
  • Expert level experience with the concepts and technologies associated with unsupervised modeling such as k-means clustering, hierarchical/agglomerative clustering, neighbors algorithms, DBSCAN, etc.
  • Demonstrated experience in guiding and mentoring junior technical staff in business interactions and model building.
  • Demonstrated ability to communicate ideas with team members and/or business leaders to convey and present very technical information to an audience that may have little or no understanding of technical concepts in data science.
  • A strong track record of communicating results, insights, and technical solutions to Senior Executive Management (or equivalent).
  • Extensive technical skills, consulting experience, and business savvy to interface with all levels and disciplines within the organization.

What sets you apart:

  • An advanced degree (Master's/PhD) in STEM, Computer Science, Statistics, Economics, or Finance is required. An additional degree in Information Technology (IT) or a closely related field is also strongly preferred, or a dual degree/major in one of these areas alongside a quantitative discipline.
  • Demonstrated ability to translate complex data analysis into actionable business insights and strategic recommendations.
  • Extensive experience in AI/ML/Data Science, focused on product development, strategy, or modeling.
  • Experience from leading tech companies (e.g., Apple, Meta, Amazon AWS, Google, FICO) in AI, automation, or digital product teams.
  • Served as an AI solution designer, developing and implementing AI solutions across diverse platforms and within complex data infrastructures.
  • Demonstrated hands-on experience with systems integration and the implementation of complex AI solutions.
  • Experience in IT roles or IT-adjacent functions, demonstrating a strong understanding of technology systems and infrastructure, is a plus.
  • Previous exposure to or understanding of the financial services sector is highly preferred.
  • Proven success in applying AI (especially GenAI) to enhance productivity, automate processes, reduce costs, or generate revenue.


 

Compensation range: The salary range for this position is: $164,780 - $314,960.

USAA does not provide visa sponsorship for this role. Please do not apply for this role if at any time (now or in the future) you will need immigration support (i.e., H-1B, TN, STEM OPT Training Plans, etc.).

Compensation: USAA has an effective process for assessing market data and establishing ranges to ensure we remain competitive. You are paid within the salary range based on your experience and market data of the position. The actual salary for this role may vary by location.

Employees may be eligible for pay incentives based on overall corporate and individual performance and at the discretion of the USAA Board of Directors.

The above description reflects the details considered necessary to describe the principal functions of the job and should not be construed as a detailed description of all the work requirements that may be performed in the job.

Benefits: At USAA our employees enjoy best-in-class benefits to support their physical, financial, and emotional wellness. These benefits include comprehensive medical, dental and vision plans, 401(k), pension, life insurance, parental benefits, adoption assistance, paid time off program with paid holidays plus 16 paid volunteer hours, and various wellness programs. Additionally, our career path planning and continuing education assists employees with their professional goals.

For more details on our outstanding benefits, visit our benefits page on USAAjobs.com.

Applications for this position are accepted on an ongoing basis, this posting will remain open until the position is filled. Thus, interested candidates are encouraged to apply the same day they view this posting.

 

USAA is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran.


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