UNKNOWN

FEED

Responsibilities include developing and validating multi-domain modeling frameworks, leading federally funded projects, building multidisciplinary teams, mentoring researchers, and engaging with external partners to advance grid reliability and resilience. Qualifications require a PhD with 5+ years of relevant experience (or equivalent education and experience), with preferred expertise in grid dynamics, co-simulation tools like HELICS or GridLAB-D, and experience with DERs and energy technologies.

YEAR

Seeking a Principal Research Engineer with expertise in power system modeling, simulation, and resilience to lead R&D efforts bridging transmission and distribution systems. Responsibilities include developing and validating multi-domain modeling frameworks, leading federally funded projects, building multidisciplinary teams, mentoring researchers, and engaging with external partners to advance grid reliability and resilience. Qualifications require a PhD with 5+ years of relevant experience (or equivalent education and experience), with preferred expertise in grid dynamics, co-simulation tools like HELICS or GridLAB-D, and experience with DERs and energy technologies. The role involves publishing research, representing the organization at conferences, and maintaining security clearances, including background checks and drug testing. Benefits include comprehensive health plans, retirement options, paid time off, and flexible work arrangements. The salary range is $173,400 to $274,100 annually.

Pacific Northwest National Laboratory

Principal Research Engineer - Grid Modeling

Background Investigation

Conferences

Cross-Functional

Mentoring

Research & Development (R&D)

Security Clearance

Simulation

Team Lead/Manager

Hire Recruits

OTHER

This position is virtual and open to residents of the 50 states, D.C. COMPENSATION & BENEFITS: Stride, Inc. considers a person's education, experience, and qualifications, as well as the position's work location, expected quality and quantity of work, required travel (if any), external market and internal value when determining a new employee's salary level. This position will research, collect, review and monitor financial data in order to build timely and accurate financial models that are able to increase company profit through enhancements of operational efficiencies and resource utilization.

Job Description SUMMARY: The Financial Analyst is responsible for providing financial analysis, budgeting, and forecasting for multiple Business Units. This position will research, collect, review and monitor financial data in order to build timely and accurate financial models that are able to increase company profit through enhancements of operational efficiencies and resource utilization. This person is someone who has excellent problem solving skills, thrives in a challenging environment, energetic, flexible, and self-motivated. ESSENTIAL FUNCTIONS: Reasonable accommodations may be made to enable individuals with disabilities to perform the essential duties. <ul><li>Budget and cost reviews including analyzing variances between actuals, forecast, and plan, use insight to iterate on forecasts and push for operational improvements</li><li>Update forecasting and budget projections regularly</li><li>Recommends actions by analyzing and interpreting data and making comparative analyses</li><li>Financial analysis and modeling - pull and consolidate large data sets, build models from scratch and improve existing ones</li><li>Provides accurate and appropriate materials to meet the monthly close timeline</li><li>Documents key financial processes, identifies and implements process improvements</li><li>Collaborate with business leads to summarize and analyze key data</li><li>Ability to consolidate large sets of analytical data and present in an easily interpreted format to drive better business decisions</li><li>Special projects - Lead special analyses and run projects to assist leadership with decision making</li><li>Support Corporate Finance group with K12 and school financial policies, internal controls, and business processes</li></ul> Supervisory Responsibilities: This position has no formal supervisory responsibilities. MINIMUM REQUIRED QUALIFICATIONS: <ul><li>Bachelor's degree in Accounting, Finance, Mathematics, or relevant field AND</li><li>Two (2) years in relevant experience OR</li><li>Equivalent combination of education and experience</li></ul> Certificates and Licenses: None required OTHER REQUIRED QUALIFICATIONS: <ul><li>Experience and knowledge in several functions within Finance (Budgeting, Consolidation, Performance Reporting and financial analysis)</li><li>Understanding of accounting processes, procedures and internal controls</li><li>Strong research and analysis skills</li><li>Able to work well as part of a team; customer service orientation</li><li>Possess very good communication skills - written and verbal</li><li>Excellent time management and organizational skills</li><li>Microsoft Office (Outlook, Word, Excel, PowerPoint, Project, Visio, etc.); Web proficiency.</li><li>Ability to travel <10% of the time</li><li>Ability to clear required background check</li></ul> WORK ENVIRONMENT: The work environment characteristics described here are representative of those an employee encounters while performing the essential functions of this job. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions. This position is virtual and open to residents of the 50 states, D.C. COMPENSATION & BENEFITS: Stride, Inc. considers a person's education, experience, and qualifications, as well as the position's work location, expected quality and quantity of work, required travel (if any), external market and internal value when determining a new employee's salary level. Salaries will differ based on these factors, the position's level and expected contribution, and the employee's benefits elections. Offers will typically be in the bottom half of the range. We anticipate the salary range to be $47,075.25 - $81,268.80. Eligible employees may receive a bonus. This salary is not guaranteed, as an individual's compensation can vary based on several factors. These factors include, but are not limited to, geographic location, experience, training, education, and local market conditions. Stride offers a robust benefits package for eligible employees that can include health benefits, retirement contributions, and paid time off. Job Type Regular The above job is not intended to be an all-inclusive list of duties and standards of the position. Incumbents will follow any other instructions, and perform any other related duties, as assigned by their supervisor. All employment is "at-will" as governed by the law of the state where the employee works. It is further understood that the "at-will" nature of e

Stride, Inc.

FP&A Analyst

Accounting

Accounting Close

Accounting Policies

Actuals

Analysis Skills

Budget Forecasting

Budgeting

Business Processes

Communication Skills

Comparative Analysis

Compensation and Benefits

Corporate Finance

Cost Analysis

Customer Support/Service

Data Analysis

Data Sets

Finance

Financial Analysis

Financial Modeling

Financial Planning and Analysis (FP&A)

Financial Policies

Financial Projections

Forecasting

Leadership

Mathematics

Microsoft Excel

Microsoft Office

Microsoft Outlook

Microsoft PowerPoint

Microsoft Visio

Microsoft Word

Operational Improvement

Operations Processes

Organizational Skills

Performance Analysis

Presentation/Verbal Skills

Problem Solving Skills

Process Improvement

Project Evaluation

Research Skills

Resource Utilization

Team Player

Time Management

University/School Policies

Variance Analysis

Willing to Travel

Writing Skills

Broadbean

This role is a specialized technical position focused on applying AI + Physics into predictive modeling, experimental design, and Bayesian optimization to enable faster, more confident decisions in new product and material development. Ability to translate advanced quantitative methods into practical workflows including proof-of-concept full-stack (backend + frontend) applications that inform technology and product decisions.

Overview:
Celanese Engineered Materials is seeking an Engineer, Digital Innovation – Predictive Modeling & Advanced Experimentation role. This role is a specialized technical position focused on applying AI + Physics into predictive modeling, experimental design, and Bayesian optimization to enable faster, more confident decisions in new product and material development. This role is an opportunity to play a key role in advancing predictive modeling and advanced experimental strategy to accelerate the design and development of next‑generation materials. Applying rigorous quantitative methods to enable informed decision‑making early in technology and product development. The role operates at the intersection of modeling, statistics, and machine learning, with a strong emphasis on translating these capabilities into practical approaches that support technology and innovation programs. This position also builds and deploys digital methods to guide experimentation, prediction, and optimization that support computer aided engineering and new product development efforts. **Location can be hybrid in one of the following locations: <ul><li>Wilmington, DE</li><li>Florence, KY</li><li>Auburn Hills, MI</li><li>Irving, TX</li></ul>

Responsibilities:
Predictive Modeling for Material Property Design<ul><li>Develop and apply predictive and hybrid machine learning approaches for the prediction of properties key to designing the next generation of materials.</li><li>Integrate mechanistic understanding, statistical modeling, and data‑driven methods to generate reliable, decision‑ready predictions.</li><li>Quantify model confidence and limitations to support risk‑aware technical decisions.</li><li>Translate complex modeling outputs into clear, actionable insights for technology and innovation stakeholders.</li></ul>Experimental Design & Bayesian Optimization for New Product Development<ul><li>Design and apply advanced experimental design strategies and Bayesian optimization for new product development.</li><li>Efficiently explore high‑dimensional design spaces to prioritize experiments and identify optimal candidates for laboratory evaluation.</li><li>Apply adaptive and sequential learning approaches to balance exploration and exploitation under limited data conditions.</li></ul>

Qualifications:
<ul><li style="font-size:10pt">Master's Degree or higher, or with equivalent experience in computer science, computer engineering, machine learning, physics, applied mathematics or related field </li><li style="font-size:10pt">Understanding of advanced materials, chemical processes, and laboratory data is a plus.</li><li style="font-size:10pt">1+ years' work experience with modeling development, data analysis, business communication, and digital transformation is highly desirable.</li><li style="font-size:10pt">Proficiency in AI + physics-based machine learning.</li><li style="font-size:10pt">Working understanding of material science fundamentals</li><li style="font-size:10pt">Strong foundation in applied statistics, experimental design, and probabilistic modeling.</li><li style="font-size:10pt">Expertise in predictive modeling and simulation for material or system property prediction.</li><li style="font-size:10pt">Experience with uncertainty quantification, model validation, and decision support under uncertainty.</li><li style="font-size:10pt">Ability to translate advanced quantitative methods into practical workflows including proof-of-concept full-stack (backend + frontend) applications that inform technology and product decisions.</li><li style="font-size:10pt">Working across the full lifecycle: problem formulation model and strategy development application and adoption.</li><li style="font-size:10pt">Communicating complex modeling and experimental concepts clearly to diverse technical audiences.</li><li style="font-size:10pt">Influencing technology and innovation decisions through quantitative, model‑driven insight.</li><li style="font-size:10pt">Operating effectively in cross‑functional environments spanning product development, technology, innovation, and digital teams.</li></ul>

Celanese International Corporation

Digital Innovation Engineer

Artificial Intelligence (AI)

Bayesian Networks

Chemical Processes

Computer Aided Engineering (CAE)

Computer Engineering

Computer Science

Computer Skills

Data Modeling

Data Processing

Decision Support

Establish Priorities

Experiment Design

Laboratory Analysis

Machine Learning

Material Science

Model Validation

Physics

Predictive Modeling

Product Development

Proof of Concept

Risk

Statistical Modeling

Statistics

Strategic Planning

Technical Support

Transformational Communications

User Interface/Experience (UI/UX)

[AW]-iCims

FULL_TIME

Celanese Engineered Materials is seeking an Engineer, Digital Innovation – Predictive Modeling & Advanced Experimentation role. This role is a specialized technical position focused on applying AI + Physics into predictive modeling, experimental design, and Bayesian optimization to enable faster, more confident decisions in new product and material development.This role is an opportunity to play a key role in advancing predictive modeling and advanced experimental strategy to accelerate the design and development of next‑generation materials. Applying rigorous quantitative methods to enable informed decision‑making early in technology and product development.The role operates at the intersection of modeling, statistics, and machine learning, with a strong emphasis on translating these capabilities into practical approaches that support technology and innovation programs. This position also builds and deploys digital methods to guide experimentation, prediction, and optimization that support computer aided engineering and new product development efforts.**Location can be hybrid in one of the following locations: <ul><li>Wilmington, DE</li><li>Florence, KY</li><li>Auburn Hills, MI</li><li>Irving, TX</li></ul>Responsibilities Predictive Modeling for Material Property Design<ul><li>Develop and apply predictive and hybrid machine learning approaches for the prediction of properties key to designing the next generation of materials.</li><li>Integrate mechanistic understanding, statistical modeling, and data‑driven methods to generate reliable, decision‑ready predictions.</li><li>Quantify model confidence and limitations to support risk‑aware technical decisions.</li><li>Translate complex modeling outputs into clear, actionable insights for technology and innovation stakeholders.</li></ul>Experimental Design & Bayesian Optimization for New Product Development<ul><li>Design and apply advanced experimental design strategies and Bayesian optimization for new product development.</li><li>Efficiently explore high‑dimensional design spaces to prioritize experiments and identify optimal candidates for laboratory evaluation.</li><li>Apply adaptive and sequential learning approaches to balance exploration and exploitation under limited data conditions.</li></ul>Qualifications  <ul><li>Master's Degree or higher, or with equivalent experience in computer science, computer engineering, machine learning, physics, applied mathematics or related field </li><li>Understanding of advanced materials, chemical processes, and laboratory data is a plus.</li><li>1+ years' work experience with modeling development, data analysis, business communication, and digital transformation is highly desirable.</li><li>Proficiency in AI + physics-based machine learning.</li><li>Working understanding of material science fundamentals</li><li>Strong foundation in applied statistics, experimental design, and probabilistic modeling.</li><li>Expertise in predictive modeling and simulation for material or system property prediction.</li><li>Experience with uncertainty quantification, model validation, and decision support under uncertainty.</li><li>Ability to translate advanced quantitative methods into practical workflows including proof-of-concept full-stack (backend + frontend) applications that inform technology and product decisions.</li><li>Working across the full lifecycle: problem formulation → model and strategy development → application and adoption.</li><li>Communicating complex modeling and experimental concepts clearly to diverse technical audiences.</li><li>Influencing technology and innovation decisions through quantitative, model‑driven insight.</li><li>Operating effectively in cross‑functional environments spanning product development, technology, innovation, and digital teams.</li></ul>  Powered by JazzHR

Celanese

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Principal Research Engineer - Grid Modeling Pacific Northwest National Laboratory

FP&A Analyst Stride, Inc.

Digital Innovation Engineer Celanese International Corporation

Digital Innovation Engineer Celanese

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