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Decision Engineer Jobs in Raleigh, NC (NOW HIRING)

Validation & Engineering Group, Inc. (V&EG) is a leading services supplier who provides solutions ... Apply statistical analysis and SPC systems to support data-driven decision-making. * Manage ...

Manufacturing Engineer

Timberlake, NC ยท On-site

$46K - $59K/yr

Analyze production data and develop reports to support decision-making. * Support ERP transactions ... Bachelor's degree in Manufacturing, Mechanical, Industrial, Electrical Engineering, or related ...

Manufacturing Engineer

Timberlake, NC

$46K - $59K/yr

Analyze production data and develop reports to support decision-making. * Support ERP transactions ... Bachelor's degree in Manufacturing, Mechanical, Industrial, Electrical Engineering, or related ...

AI Automation Engineer V

Durham, NC ยท Remote

$126K - $244K/yr

While Bitovi will conduct the early rounds, the final interview and hiring decision will be made by Avalara directly. What You'll Do As an AI Automation Engineer, you will help shape how work is ...

Our team integrates cutting-edge technologies into the construction process to streamline operations, enhance decision-making, and drive efficiency at all levels. We are looking for a MLOps Engineer ...

It is a ground-up rebuild of the decision-making machinery behind American healthcare, at national ... As an Agentic AI Engineer, you will design, build, and operationalize the LLM- and SLM-powered ...

MLOps Engineer DPR is a leading construction company committed to delivering high-quality ... decision-making, and drive efficiency at all levels. We are looking for aMLOps Engineerto join our ...

Asset Management Senior Engineer

Zebulon, NC ยท On-site

$88K - $121K/yr

Python) to improve diagnostics and decision-making. * Use cross sites scorecards to enable benchmarking across sites. * Animate the reliability engineering COP, ensuring consistent development of ...

AI Engineer

Raleigh, NC ยท On-site

$55K - $187K/yr

... decision-making and driving business growth. Within our Internal Firm Services practice, you will apply data, algorithms, and software engineering to build and deploy software and platform systems ...

Senior Electrical Engineer

Durham, NC ยท On-site

$97K - $126K/yr

Electrical Engineer Duration: 12 Months Contract - Possible extension Location: Durham, NC Primary ... This role requires strong technical expertise, independent decision-making, and the ability to ...

New

Sr. Dam and Levee RIDM Engineer

Raleigh, NC ยท On-site

$101K - $139K/yr

Help build Freese and Nichols' Risk Informed Decision Making (RIDM) practices and resources for dam and levee safety and engineering. * Serve as Project Manager for dam assessment and rehabilitation ...

Sr. Dam and Levee RIDM Engineer

Raleigh, NC ยท Hybrid

$101K - $139K/yr

Help build Freese and Nichols' Risk Informed Decision Making (RIDM) practices and resources for dam and levee safety and engineering. * Serve as Project Manager for dam assessment and rehabilitation ...

Showing results 21-40

Decision Engineer information

See Raleigh, NC salary details

$35.5K

$104.3K

$133.7K

How much do decision engineer jobs pay per year?

As of Aug 7, 2026, the average yearly pay for decision engineer in Raleigh, NC is $104,286.00, according to ZipRecruiter salary data. Most workers in this role earn between $86,000.00 and $132,200.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a decision engineer, and why are they important?

To thrive as a Decision Engineer, you need strong analytical skills, expertise in data modeling, and a background in fields like operations research, mathematics, or computer science. Familiarity with decision analysis tools, optimization software (such as CPLEX or Gurobi), and programming languages like Python or R is typically required. Exceptional problem-solving abilities, communication skills, and the capacity to synthesize complex information are valuable soft skills in this role. These competencies enable Decision Engineers to develop effective solutions for complex business challenges and drive data-informed decision-making.

How does a decision engineer typically collaborate with data scientists and business stakeholders to deliver impactful solutions?

Decision Engineers frequently act as a bridge between technical teams and business stakeholders. In a typical workflow, they collaborate with data scientists to understand the underlying data models and analytical outputs, then work closely with business leaders to translate these insights into actionable strategies. This often involves facilitating discussions to clarify business objectives, ensuring analytical approaches align with end goals, and iteratively refining solutions based on feedback. Strong communication and project management skills are essential, as Decision Engineers must synthesize complex information and drive consensus among diverse teams.

What is a decision engineer?

Decision Engineers are professionals who apply analytical, mathematical, and computational techniques to help organizations make data-driven decisions. They often use tools from operations research, data science, and systems engineering to evaluate complex options, optimize processes, and predict outcomes. Their work enables businesses to solve challenging problems, improve efficiency, and minimize risks in decision-making processes.

What is the difference between Decision Engineer vs Data Scientist?

AspectDecision EngineerData Scientist
Required credentialsBachelor's or master's in engineering, analytics, or related fieldsBachelor's or master's in statistics, computer science, or related fields
Work environmentFocus on designing decision models, algorithms, and optimization processesFocus on analyzing data, building predictive models, and extracting insights
Employer and industry usageUsed in industries like manufacturing, finance, and logistics for decision automationCommon in tech, finance, healthcare for data analysis and modeling

Decision Engineers primarily develop decision models and optimize processes to improve business outcomes, while Data Scientists analyze data to generate insights and predictive models. Both roles require strong analytical skills, but Decision Engineers focus more on decision automation and operational efficiency, whereas Data Scientists focus on data analysis and modeling.

What cities near Raleigh, NC are hiring for Decision Engineer jobs? Cities near Raleigh, NC with the most Decision Engineer job openings:

Sr Staff Product Engineer

Renesas Electronics

Morrisville, NC โ€ข On-site

Full-time

Re-posted 11 days ago


Job description

Job Description

Applicants for this position must be currently authorized to work in the United States on a full-time basis. Renesas is unable to sponsor applicants for work visas for this position now or in the future.   

Hybrid - Morrisville, NC

Renesas is a global leader in semiconductor solutions, enabling innovations across industrial, IoT, edge computing, and intelligent power applications. Our teams drive the development of next-generation products, including devices supporting Edge AI capabilities, by collaborating across design, product engineering, and manufacturing to deliver high-quality, scalable solutions worldwide.

Key Responsibilities

Product Leadership & NPI Execution

  • Lead end-to-end product lifecycle execution across multiple programs—from concept definition through characterization, qualification, customer release, and ramp to high-volume manufacturing (HVM).
  • Define and drive product validation, characterization, and qualification strategies aligned with product requirements, reliability expectations, and customer use cases.
  • Demonstrate a proven track record of successfully releasing multiple IC products into production and sustaining performance through volume ramp.

Data Analysis, Characterization & Yield Strategy

  • Apply advanced statistical analysis and data science techniques to characterize device electrical performance and parametric behavior.
  • Develop robust methodologies for analyzing distributions, corner performance, and guard band optimization.
  • Lead deep-dive investigations of yield excursions, parametric shifts, and failure mechanisms using structured statistical approaches and large-scale data analysis.
  • Identify correlations across design, silicon, and test datasets to uncover root causes and improve product robustness.
  • Establish scalable analytics frameworks, dashboards, and visualization tools to enable data-driven decision making across product lifecycle phases.

Qualification, Reliability, ESD & Latch-Up Expertise

  • Define and execute comprehensive product qualification strategies aligned to JEDEC and industry standards (e.g., JESD47, JESD22 series).
  • Drive reliability stress planning and interpretation, including HTOL, HAST/uHAST, TC, ELFR, and associated qualification methodologies.
  • Lead ESD and latch-up qualification strategy, data analysis, and failure resolution in alignment with product requirements.
  • Analyze reliability data to assess failure mechanisms, lifetime projections, and margin to specification limits.
  • Ensure qualification coverage, sample sizes, and stress conditions support defensible product release decisions.
  • Partner with reliability and quality teams to resolve qualification risks and define mitigation strategies.

Failure Analysis & Root Cause Investigation

  • Lead complex failure analysis activities across electrical, parametric, ESD, latch-up, and reliability-related failures.
  • Utilize data-driven approaches to correlate failure signatures with design, process, or test-related mechanisms.
  • Drive cross-functional root cause investigations and ensure corrective actions are implemented and verified.
  • Develop systematic approaches to failure classification, screening effectiveness, and defect pareto analysis.

AI-Driven Engineering Efficiency

  • Identify and drive opportunities to improve engineering efficiency through application of AI, machine learning, and advanced analytics in areas such as:
    • Characterization data reduction and automation
    • Anomaly detection and outlier classification
    • Predictive yield and reliability modeling
  • Develop or leverage intelligent workflows to accelerate insight generation and reduce manual analysis effort.
  • Promote adoption of data-centric and AI-assisted methodologies to improve engineering productivity and decision quality.

Technical Leadership & Cross-Functional Influence

  • Serve as a recognized subject matter expert in product engineering, statistical analysis, reliability, and failure analysis.
  • Lead cross-functional efforts across design, applications, reliability, and test teams to resolve highly complex technical challenges.
  • Provide leadership in defining characterization plans, qualification strategies, and analysis methodologies.
  • Mentor engineers in advanced statistical techniques, reliability interpretation, and structured problem solving.

Strategic Problem Solving & Innovation

  • Work on complex, ambiguous problems requiring evaluation of incomplete or conflicting data, applying conceptual and statistical thinking to determine optimal solutions.
  • Anticipate technical risks in product performance, qualification adequacy, and reliability margins, and proactively drive improvements.
  • Contribute to development of best practices in qualification methodology, data analysis, and engineering decision frameworks.

Stakeholder Engagement & Organizational Impact

  • Build and lead networks across global teams to align characterization strategy, qualification coverage, and product readiness.
  • Communicate complex analytical findings, qualification results, and failure analysis conclusions to diverse stakeholders, including senior leadership.
  • Influence product release decisions through data-driven insight, technical expertise, and sound engineering judgment.
  • Act as a key authority on product readiness, with accountability for decisions impacting product quality and business outcomes.
Qualifications

Applicants for this position must be currently authorized to work in the United States on a full-time basis. Renesas is unable to sponsor applicants for work visas for this position now or in the future.   

Technical Qualifications

  • Min Education: Bachelor’s of Science in Electrical or Microelectronics Engineering
  • Experience:
    • 10+ years of experience with a Bachelor’s degree
    • 8+ years with a Master’s degree
    • 5+ years with a PhD

Required Experience & Expertise

  • Proven success in releasing multiple semiconductor products from concept through qualification and into HVM.
  • Deep expertise in statistical analysis, including distribution analysis, correlation analysis, and limit optimization.
  • Strong background in product characterization and electrical performance evaluation.
  • Extensive experience in reliability qualification, ESD, and latch-up methodologies.
  • Demonstrated expertise in failure analysis and root cause investigation across multiple failure modes.
  • Proven leadership in solving highly complex technical problems using data-driven approaches.
  • Experience influencing engineering decisions and leading cross-functional technical initiatives.

Preferred Qualifications

  • Strong working knowledge of JEDEC standards (e.g., JESD47, JESD22 series) and industry qualification practices.
  • Hands-on experience with ESD qualification (HBM, CDM) and latch-up testing/analysis.
  • Experience with reliability stress planning and interpretation (HTOL, HAST, TC, ELFR, etc.).
  • Experience with Edge AI-enabled products or data-centric semiconductor applications.
  • Familiarity with applying machine learning or AI techniques to engineering data analysis workflows.
  • Proficiency with JMP, Python, or other advanced data analysis and visualization tools.
  • Demonstrated success driving efficiency improvements in characterization, qualification, and yield analysis workflows.

Additional Information

Renesas is an embedded semiconductor solution provider driven by its Purpose, To Make Our Lives Easier. With a global team of over 21,000 engineers and problem solvers in more than 30 countries, we offer the opportunity to work on world‑leading technology for Automotive, Industrial, Infrastructure, and IoT, shaping a safer, healthier, greener, and smarter future.

At Renesas, TAGIE is our culture, grounded in being Transparent, Agile, Global, Innovative, and Entrepreneurial. It shapes how we work, grow and deliver on our purpose together. This collaborative spirit and mindset drive our semiconductor technology to transform industries and impact millions of lives.

We believe in rewarding our employees with a competitive benefits package alongside their salary. More information will be provided during the hiring process.

Are you ready to join our team and shape the future with us?

Renesas Electronics is an equal opportunity and affirmative action employer, committed to supporting diversity and fostering a work environment free of discrimination on the basis of sex, race, religion, national origin, gender, gender identity, gender expression, age, sexual orientation, military status, veteran status, or any other basis protected by law. For more information, please read our Diversity & Inclusion Statement.

Renesas Electronics deals with dual-use technology that is subject to U.S. export controls regulations. Under these regulations it may be necessary for Renesas to obtain U.S. government export license prior to release of technology to certain persons. The decision whether or not to file or pursue an export license application is at the sole discretion of Renesas.