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Junior Statistical Analyst Jobs in Arizona (NOW HIRING)

Osaic

Quantitative Analyst I

Scottsdale, AZ · Hybrid

$57K - $63K/yr

As a junior analyst, you will work closely with senior team members to learn analytical frameworks ... Bachelor's degree in economics, statistics, data science, computer science, or a related field ...

Advisor Group

Quantitative Analyst I

Scottsdale, AZ · On-site

$57K - $63K/yr

As a junior analyst, you will work closely with senior team members to learn analytical frameworks ... Bachelor's degree in economics, statistics, data science, computer science, or a related field ...

Osaic

Quantitative Analyst I

Scottsdale, AZ · On-site

$57K - $63K/yr

As a junior analyst, you will work closely with senior team members to learn analytical frameworks ... Bachelor's degree in economics, statistics, data science, computer science, or a related field ...

Smart Synergies

Cost Engineer

Chandler, AZ · On-site

... junior Cost Engineers with multiple backgrounds and experience. * Experience with preparation of cost studies utilizing historical data, statistical analysis, and cost and quantity comparisons.

Osaic

Quantitative Analyst I

Scottsdale, AZ · Hybrid

$57K - $63K/yr

... junior analyst, you will work closely with senior team members to learn analytical frameworks ... Bachelor's degree in economics, statistics, data science, computer science, or a related field ...

Osaic

Quantitative Analyst I

Scottsdale, AZ · Hybrid

$57K - $63K/yr

As a junior analyst, you will work closely with senior team members to learn analytical frameworks ... Bachelor's degree in economics, statistics, data science, computer science, or a related field ...

Republic Services

Data Scientist III

Phoenix, AZ

... junior data scientists, and a trusted analytical partner to business stakeholders by clearly ... Designs and builds advanced statistical, predictive, machine learning, and AI models, selecting ...

Republic Services

Data Scientist III

Phoenix, AZ · On-site

... junior data scientists, and a trusted analytical partner to business stakeholders by clearly ... Designs and builds advanced statistical, predictive, machine learning, and AI models, selecting ...

Republic Services

Data Scientist III

Phoenix, AZ

... junior data scientists, and a trusted analytical partner to business stakeholders by clearly ... Designs and builds advanced statistical, predictive, machine learning, and AI models, selecting ...

Visium Resources, Inc.

Cost Engineer

Chandler, AZ

... junior Cost Engineers with multiple backgrounds and experience. * Experience with preparation of cost studies utilizing historical data, statistical analysis, and cost and quantity comparisons.

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Junior Statistical Analyst information

What is a junior statistical analyst?

Junior Statistical Analysts are entry-level professionals who collect, analyze, and interpret data to help organizations make informed decisions. They use statistical methods and software tools to identify trends, create reports, and support senior analysts or data scientists. Their work often involves preparing data, running basic statistical tests, and presenting findings in clear, actionable formats. Junior Statistical Analysts typically work under supervision while gaining experience and developing their analytical skills.

What are some common challenges a junior statistical analyst may face when transitioning from academic work to a professional analytics team?

One common challenge for Junior Statistical Analysts is adapting to the fast-paced nature of business environments, where deadlines can be tight and priorities may shift quickly. Unlike academic projects, real-world data is often messy and incomplete, requiring resourcefulness in data cleaning and validation. Additionally, analysts must learn to communicate technical findings in a clear, actionable manner for non-technical stakeholders, which is a skill that develops with experience. Collaborating with cross-functional teams and understanding business objectives are also crucial aspects that may differ from academic settings.

What are the key skills and qualifications needed to thrive as a junior statistical analyst, and why are they important?

To thrive as a Junior Statistical Analyst, you need a solid background in statistics, data analysis, and mathematics, usually supported by a bachelor’s degree in a related field. Familiarity with statistical software such as R, SAS, SPSS, or Python, as well as proficiency in Excel, is typically required. Strong attention to detail, critical thinking, and clear communication skills help you interpret data and present findings effectively. These skills ensure accurate analysis, actionable insights, and effective collaboration with stakeholders across projects.

What is the difference between Junior Statistical Analyst vs Data Analyst?

AspectJunior Statistical AnalystData Analyst
Required CredentialsBachelor's in Statistics, Mathematics, or related field; some roles may require internshipsBachelor's in Data Science, Statistics, or related field; often includes certifications in data tools
Work EnvironmentEntry-level, team-based, often in finance, healthcare, or tech industriesVaries from entry to senior levels; corporate, consulting, or tech settings
Employer & Industry UsageCommon in research, finance, healthcare, and government sectorsWidely used across industries including marketing, finance, tech, and consulting

While both roles involve data handling and analysis, Junior Statistical Analysts focus more on statistical methods and data modeling, whereas Data Analysts often work with broader data management and visualization tasks. The roles are closely related, with overlapping skills and work environments, but differ slightly in focus and scope.

What are the most commonly searched types of Statistical Analyst jobs in Arizona?

The most popular types of Statistical Analyst jobs in Arizona are:

Junior Statistical Analyst jobs near you
Infographic showing various Junior Statistical Analyst job openings in Arizona as of August 2026, with employment types broken down into 87% Full Time, 6% Part Time, 2% Temporary, and 5% Contract. Highlights an 80% Physical, 9% Hybrid, and 11% Remote job distribution.

Scientific Analyst II

University of Arizona

Tucson, AZ • On-site

Full-time

Re-posted 5 days ago

University Of Arizona rating

7.3

Company rating: 7.3 out of 10

Based on 68 frontline employees who took The Breakroom Quiz

364th of 622 rated colleges and universities

Job description

Data Analysis and Machine Learning Pipeline Development:

  • Under moderate guidance collaborate in the design, develop, and execution of machine learning and AI-driven analytical pipelines to analyze large-scale biomedical datasets from UK Biobank, All of Us, Insight, and electronic medical records.
  • Apply supervised and unsupervised machine learning algorithms (e.g., logistic regression, random forests, deep learning) to identify risk factors, biomarkers, and patterns associated with neurodegenerative diseases and the effects of menopausal hormone therapy (MHT) on brain health.
  • Collaborate on the development and validation of predictive models integrating genomic, clinical, lifestyle, and imaging data using general knowledge of principals, theories and concepts.

Drug Repurposing Research and Bioinformatics Analysis:

  • Collaborating in computational drug repurposing analyses to identify existing FDA-approved compounds with potential efficacy for AD, PD, MS, and ALS prevention and treatment. Integrate multi-omics data (genomics, transcriptomics, proteomics) with clinical outcomes data to prioritize drug candidates.
  • Collaborate with wet lab and clinical teams to support translational interpretation of findings.

Epidemiological and Clinical Data Management and Harmonization:

  • Access, curate, harmonize, and manage large population-based datasets including UK Biobank, All of Us, and institutional EMR data.
  • Ensure data quality, reproducibility, and compliance with data use agreements and IRB protocols.
  • Collaborate in the develop and maintenance of reproducible data pipelines using Python, R, and high performance computer.
  • Perform statistical analyses including survival analysis, longitudinal modeling, and causal inference.

Scientific Communication, Dissemination, and Collaboration:

  • Compare and contribute to peer-reviewed manuscripts, conference presentations, and grant applications reporting research findings on MHT, menopause, and neurodegenerative disease.
  • Present results to interdisciplinary research teams, departmental seminars, and external stakeholders.
  • Collaborate closely with Dr. Francesca Vitali, co-investigators, and consortium partners. Maintain thorough documentation of analytical methods to ensure transparency and reproducibility.
  • Participate in lab meetings, journal clubs, and professional development activities.

Research Infrastructure and Continuous Improvement:

  • Maintain and improve lab computational infrastructure, including code repositories (GitHub), analytical workflows, and documentation standards.
  • Evaluate and adopt emerging AI/ML tools and methodologies relevant to brain science research.
  • Assist in training junior lab members or graduate students on data science methods and tools as needed.
  • Stay current with literature in neurodegenerative disease, computational.

Knowledge, Skills and Abilities:

  • Strong theoretical and applied knowledge of machine learning, deep learning, and statistical modeling.
  • Strong data wrangling and preprocessing skills for large, heterogeneous datasets.
  • Expert-level programming skills in Python and/or R; proficiency with ML libraries (scikit-learn, TensorFlow, PyTorch, XGBoost).
  • Knowledge of drug repurposing methodologies or network pharmacology.
  • Knowledge and familiarity with electronic medical records data analysis.
  • Knowledge and proficiency with SQL and database management.
  • Ability to collaborate effectively within interdisciplinary teams spanning data science, neuroscience, clinical research, and epidemiology.
  • Ability to manage multiple concurrent projects and meet deadlines.
  • Ability to critically evaluate scientific literature and translate findings into research hypotheses and analytical strategies.
  • Ability to communicate complex analytical results clearly to both technical and non-technical audiences.

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