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Operations Research Phd Jobs in Arizona (NOW HIRING)

Master Electronics

Data Scientist II

Phoenix, AZ · On-site

$120 - $170/hr

Advanced degree (Master's or PhD) in a relevant field (Statistics, Machine Learning, AI, Operations Research, Economics/Econometrics, etc.) * Experience with B2B or e‑commerce pricing, such as ...

Master Electronics

Data Scientist II

Phoenix, AZ · On-site

Advanced degree (Master's or PhD) in a relevant field (Statistics, Machine Learning, AI, Operations Research, Economics/Econometrics, etc.) * Experience with B2B or ecommerce pricing, such as quote ...

Master Electronics

Data Scientist II

Phoenix, AZ

Advanced degree (Master's or PhD) in a relevant field (Statistics, Machine Learning, AI, Operations Research, Economics/Econometrics, etc.) * Experience with B2B or ecommerce pricing, such as quote ...

CommonSpirit Health

Research Scientist I

Phoenix, AZ

... PhD and undergrad students. Preparation and delivery of research presentations at local meetings national and international conferences. General operation maintenance and troubleshooting of lab ...

Master Electronics

Data Scientist II

Phoenix, AZ

Advanced degree (Master's or PhD) in a relevant field (Statistics, Machine Learning, AI, Operations Research, Economics/Econometrics, etc.) * Experience with B2B or ecommerce pricing, such as quote ...

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All JobsOperations Research Phd JobsOperations Research Phd Jobs in Arizona

Operations Research Phd information

See Arizona salary details

$35.9K

$87.4K

$140.7K

How much do operations research phd jobs pay per year?

As of Aug 8, 2026, the average yearly pay for operations research phd in Arizona is $87,415.00, according to ZipRecruiter salary data. Most workers in this role earn between $62,000.00 and $107,600.00 per year, depending on experience, location, and employer.

What is an operations research PhD?

Operations Research PhDs are experts who have completed a doctoral program focused on advanced analytical methods to help make better decisions and solve complex problems. Their studies involve mathematics, statistics, computer science, and engineering principles to develop models and algorithms used in industries such as logistics, finance, healthcare, and manufacturing. With a PhD, they often pursue careers in academia, industry research, or consulting, applying their expertise to optimize systems and processes.

What are some common challenges faced by operations research PhDs when transitioning from academia to industry roles?

One common challenge for Operations Research PhDs moving into industry is adapting to the faster-paced environment where solutions often need to be practical and implemented quickly, rather than purely theoretically optimal. Additionally, communicating complex analytical methods to non-technical stakeholders and working within cross-functional teams requires strong collaboration and interpersonal skills. Industry roles may also demand proficiency with industry-specific tools and the ability to manage multiple projects with shifting priorities. Embracing these challenges can accelerate professional growth and lead to rewarding career advancement.

What are the key skills and qualifications needed to thrive as an operations research PhD?

To thrive as an Operations Research PhD, you need advanced analytical skills, a strong foundation in mathematics and statistics, and a doctoral degree in operations research or a related quantitative field. Expertise with optimization software (like CPLEX or Gurobi), programming languages (such as Python, R, or MATLAB), and familiarity with data analysis tools are typically required. Strong problem-solving abilities, effective communication, and the ability to work collaboratively distinguish top performers in this role. These skills enable professionals to develop data-driven solutions to complex business problems and communicate insights to stakeholders, driving organizational efficiency and innovation.
What are popular job titles related to Operations Research Phd jobs in Arizona? For Operations Research Phd jobs in Arizona, the most frequently searched job titles are:
What job categories do people searching Operations Research Phd jobs in Arizona look for? The top searched job categories for Operations Research Phd jobs in Arizona are:
What cities in Arizona are hiring for Operations Research Phd jobs? Cities in Arizona with the most Operations Research Phd job openings:
Operations Research Phd jobs near you
Infographic showing various Operations Research Phd job openings in Arizona as of August 2026, with employment types broken down into 83% Full Time, 13% Part Time, 1% Temporary, and 3% Contract. Highlights an 94% Physical, 2% Hybrid, and 4% Remote job distribution, with an average salary of $87,415 per year, or $42 per hour.

Scientist 2

Swoon Technology Resources LLC

Scottsdale, AZ • On-site

$75K - $81K/yr

Full-time

Re-posted 18 days ago

Job description

Location: Scottsdale,Arizona
Expected Start Date: Apr 7, 2025
Salary: $75,000 - $81,500
Bring the role to life for me:
This is a highly execution-focused quantitative research role supporting established Dreamscape Learn (DSL) studies.
  • The researcher is stepping into well-structured, well-documented projects - this is not exploratory research
  • There is extensive existing documentation, surveys, datasets, and reporting templates
  • The primary responsibility is executing analysis and producing reports, not designing new studies
  • Work is fast-paced and deadline-driven (e.g., multiple high-priority reports due at the same time)
  • Success in the first 12 months looks like:
    • Consistently delivering high-quality quantitative reports
    • Synthesizing findings into clear written takeaways
    • Managing multiple concurrent studies without getting overwhelmed
  • Once they've proven strong execution, they may gradually:
    • Pitch additional analyses
    • Contribute more meaningfully to research design
This role requires someone who is:
  • Extremely organized
  • Comfortable with some ambiguity and competing priorities
  • Strong in written communication (summarizing long-term research clearly)

  • From JD:
    • This role is primarily focused on executing applied quantitative research, with some contribution to research design and methodology as needed.
    • The researcher will support scholarly and operational research for Dreamscape Learn (DSL), ASU's virtual-reality-based learning products.
    • Work includes data extraction, cleaning, statistical analysis, visualization, survey research, reporting, and stakeholder collaboration.
    • Research is action-oriented and operational, intended to inform pedagogy, engagement, learning technology decisions, and equity outcomes.

About Dreamscape Learn (DSL)
Dreamscape Learn is a collaboration between ASU and Dreamscape Immersive, founded during COVID. It combines storytelling, virtual reality, and curriculum design to improve outcomes in high-failure-rate courses.
Examples include:
  • Biology and chemistry courses where students explore concepts (e.g., going inside atoms) through VR
  • WP Carey projects where students experience a virtual Starbucks from a supply-chain perspective, observing real-world operational dynamics (e.g., peak hours, process flow)

  • Are there active Dreamscape Learn (DSL) studies this hire would step into right away, or would they be launching new research?
    • JD:
      • ongoing mixed-methods Dreamscape Learn (DSL) studies already exist, and this person will step into active research while also supporting iterative research on newly developed DSL products.

What types of quantitative analyses does the team run most often today?
  • Descriptive statistics (primary)
  • Comparisons across student cohorts (e.g., pass/fail, demographics)
  • Outcome analysis across large student populations (e.g., 1,000+ students)
  • Occasional regression analysis (not heavy or advanced modeling)

Top 3-5 responsibilities
  1. Execute quantitative analyses in R using existing datasets and frameworks
  2. Clean, transform, and manage research data efficiently
  3. Produce clear, structured reports with written key takeaways
  4. Support multiple concurrent DSL studies and reporting timelines
  5. Collaborate internally with researchers and stakeholders (internal-facing only)

Required Qualifications - What are the true must haves - what does this person need to come in already knowing?
Hard Skills
  1. Strong quantitative data analysis skills using R
  2. Data cleaning, transformation, and visualization
  3. Experience with quantitative research methodologies
  4. Survey research experience (Qualtrics preferred)
  5. Ability to manage multiple research projects and files

Nice to Have
  1. SQL experience
  2. Experience with higher education research or institutional data
  3. Familiarity with learning science or VR-based education research

Education / Certifications
PhD required
  • Discipline must have a strong statistical foundation
  • STEM-related fields are ideal (science, technology, engineering, math)
  • Candidates must have taken statistics during their PhD and actively used R

What level of R expertise is required on day one?
  • Must be able to:
    • Independently analyze data in R
    • Write and run analysis code without assistance
    • Deliver clean, repeatable outputs
  • This cannot be someone who:
    • "Knows R conceptually"
    • Relies on ChatGPT or AI to generate code
  • They do not want to train someone on R
Nice to Have
  1. SQL experience (joins, basic querying)
  2. Experience with higher education or institutional research data
  3. Familiarity with learning science or VR-based education research

Soft Skills
  1. Strong written communication (clear synthesis of long-term research findings)
  2. Ability to stay calm and organized under heavy workload
  3. Comfortable working heads-down with limited external presentation exposure

Disqualifiers - Do NOT Want to See
  1. Candidates who cannot demonstrate real, hands-on R experience
  2. "Vibe coders" or candidates dependent on AI to write analysis code
  3. Qualitative-only researchers or tools (e.g., MaxQDA, Dedoose, ATLAS.ti)

Where Are Candidates Missing the Mark?
  • Claiming R experience but unable to demonstrate it

Screening Questions to Use Up Front
  • Walk me through how you've used R in your dissertation or recent research
  • What types of statistical analyses do you run most often in R?
  • How do you manage multiple research deadlines at once?
  • Can you share an example of a report you've produced summarizing long-term findings?
Interview Process
  • Round 1 (Panel):
    • Selena (lead)
    • Kevin (Senior Researcher)
    • Bailey (slightly more senior colleague)
  • Round 2 Annie
  • Technical Assessment:
    • R-based assignment
  • Sponsorship is possible for the right candidate, but must be discussed upfront

Apply