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Industrial Engineering Analytics Engineer (Manufacturing Systems & Modeling)

Pittsburgh, PA, US • Posted 3 hours ago • Updated 3 hours ago

Contract W2

12 Months

On-site

Depends on Experience

Fitment

Dice Job Match Score™

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Job Details

Skills

Artificial Intelligence
Industrial Engineering
Machine Learning (ML)
OEE
Warehouse
Predictive Analytics
COGS
Financial Modeling
Layout
Information Engineering

Summary

Industrial Engineering Analytics Engineer (Manufacturing Systems & Modeling) 
Location: Pittsburgh, PA (Onsite)
Experience: 8 to 10 Years

Role Summary 
Client is seeking an experienced Industrial Engineering Analytics Engineer to develop and manage advanced analytical models that drive manufacturing efficiency, capacity planning, operational performance, and cost optimization.

This role will build integrated Industrial Engineering (IE) models connecting capacity, labor, material flow, PFEP, OEE, yield, and COGS to enable data-driven decision-making across factory operations. The ideal candidate combines strong industrial engineering fundamentals with expertise in analytics, simulation modeling, financial analysis, and AI-enabled manufacturing systems.

Key Responsibilities 
Industrial Engineering Analytics & Modeling

Develop and maintain integrated IE models that connect:

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Capacity Planning
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Labor Planning
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Material Flow
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PFEP (Plan for Every Part)
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Cost of Goods Sold (COGS)
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Build and manage capacity models incorporating:

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Target vs. Forecast Capacity
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Cycle Time
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OEE
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Yield Losses
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Bottleneck Analysis
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Develop labor models to optimize:

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Headcount Planning
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Labor Utilization
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Labor Overhead (LOH)
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Workforce Productivity
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Lead COGS modeling, including:

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Direct Labor
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Manufacturing Overhead
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Scrap Costs
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Process-Driven Cost Components
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Create and evaluate business cases using:

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ROI
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IRR
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NPV
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Cost-Benefit Analysis
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Manufacturing Performance Optimization

Develop and maintain:

OEE Models (Availability, Performance, Quality)

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Yield Models
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Scrap Analysis Models
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Perform:

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Cycle Time Analysis
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Line Balancing
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Throughput Analysis
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Bottleneck Identification
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Conduct buffer and WIP analysis to optimize:

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Inline Storage
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Interline Storage
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Production Flow Stability
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Create process flow diagrams (PFDs) and value stream maps to identify operational inefficiencies.

Factory Planning & Simulation

Support factory planning, site layout, and material flow decisions using data-driven modeling.

Perform scenario analysis and sensitivity studies to evaluate:

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Production Strategies
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Capacity Expansion Plans
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Operational Trade-Offs
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Develop and utilize simulation models using tools such as:

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FlexSim
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AnyLogic
```
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Simio
```

Support factory ramp-up, installation, commissioning, and operational readiness through model validation and performance tracking.

Material Flow & PFEP Integration

Integrate PFEP data into analytical models to optimize:

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Material Flow
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Line-Side Delivery
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Storage Strategies
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Warehouse Interfaces
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Support warehouse and manufacturing integration initiatives to improve overall operational efficiency.

Data Systems & Analytics

Design scalable data models and architecture supporting:

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Capacity Analytics
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Labor Analytics
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Material Flow Analytics
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Cost Analytics
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Develop standardized frameworks and governance processes for:

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Data Modeling
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Reporting
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Analytics
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Performance Measurement
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Automate data collection, validation, and reporting pipelines.

Build executive dashboards and operational reporting systems using manufacturing and shop-floor data.

AI & Advanced Analytics

Introduce and implement AI-driven solutions to improve industrial engineering analytics and decision-making.

Develop predictive analytics capabilities for:

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Capacity Optimization
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Throughput Improvement
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Cost Reduction
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Production Performance Forecasting
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Establish best practices for:

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Data Quality
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Model Standardization
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System Integration
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Cross-Functional Collaboration

Partner with:

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Manufacturing Engineering
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Operations
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Supply Chain
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Finance
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Controls Engineering
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MES Teams
```

Integrate real-time production data into IE models.

Translate complex analytical findings into executive-level recommendations and business insights.

Required Qualifications

Bachelor s degree in:

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Industrial Engineering
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Mechanical Engineering
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Operations Research
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Related Engineering Discipline
```

7+ years of experience in:

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Industrial Engineering Analytics
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Manufacturing Modeling
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Operations Analysis
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Capacity Planning
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Strong understanding of:

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Manufacturing Systems
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Industrial Engineering Principles
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Capacity Planning
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Production Analytics
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Preferred Qualifications

Experience developing end-to-end IE models integrating:

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Capacity
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Labor
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PFEP
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Material Flow
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Cost Models
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Strong expertise in:

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Capacity Modeling
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OEE Analysis
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Cycle Time Studies
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Line Balancing
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Throughput Optimization
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Experience with:

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PFEP
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Material Flow Optimization
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Warehouse Integration
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Hands-on experience with simulation tools:

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FlexSim
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AnyLogic
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Simio
```

Strong financial modeling experience including:

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ROI
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IRR
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NPV
```
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Cost Analysis
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Knowledge of:

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COGS Modeling
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Manufacturing Cost Structures
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Financial Impact Analysis
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Experience using:

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Advanced Excel Modeling
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```
Python
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SQL
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Power BI
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Tableau
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Familiarity with:

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AI/ML Applications in Manufacturing
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Lean Manufacturing
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Continuous Improvement Methodologies
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Key Skills 
Industrial Engineering

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Capacity Planning
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Labor Modeling
```
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OEE Analysis
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```
Cycle Time Analysis
```
```
Line Balancing
```
```
Bottleneck Analysis
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Throughput Optimization
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Yield & Scrap Analysis
```

Manufacturing Systems

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Factory Modeling
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Material Flow Analysis
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PFEP
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WIP Optimization
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Warehouse Integration
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Production Planning
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Analytics & Data

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Advanced Excel
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```
Python
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```
SQL
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```
Power BI
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Tableau
```
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Data Modeling
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Dashboard Development
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Predictive Analytics
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Simulation & Optimization

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FlexSim
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AnyLogic
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```
Simio
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Scenario Analysis
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Sensitivity Analysis
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Factory Simulation
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Financial Analysis

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COGS Modeling
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ROI
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IRR
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NPV
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Cost-Benefit Analysis
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AI & Automation

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AI-Driven Analytics
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Predictive Modeling
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Automated Reporting
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Data Governance 
```

Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.

Dice Id: 10500016
Position Id: 9006037
Posted 3 hours ago

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Pittsburgh, Pennsylvania

•

Yesterday

Job Title: Industrial Engineering Analytics Engineer (Manufacturing Systems & Modeling) Location: Pittsburgh, PA (Onsite) Required Skills: Greenfield or brownfield project experience (good to have)Equipment planningCapacity planningLabour planningCAPEX management (good to have)Supplier validationCapital investments ROI, IRR, NPV, and cost-benefit analysisDesign and maintain OEE modelsSupport factory ramp-up, installation, and operational readiness through model validation and performance trackin

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Industrial Engineering Analytics Engineer (Manufacturing Systems & Modeling)

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Required Skills:Capacity modeling (OEE, cycle time, bottleneck analysis)Labor & cost modeling (COGS, LOH, ROI, NPV, IRR)Manufacturing analytics & process optimizationSimulation tools: FlexSim, AnyLogic, or SimioAdvanced Excel, SQL, Python, Power BI/TableauPFEP, material flow modeling, forecasting & scenario analysisAI/ML-based analytics (preferred)Experience:7+ years in industrial engineering analytics/manufacturing modelingExpertise in capacity, labor, and cost modelingStrong factory operations

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Contract, Third Party

60 - 65

Manufacturing Analytics Engineer

Pittsburgh, Pennsylvania

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715 years Industrial Engineer / Manufacturing Analytics Engineer / Operations Research Engineer with strong experience in factory modeling, capacity planning, simulation tools, and manufacturing cost analytics. The client is looking for someone who can build data-driven factory models and improve manufacturing operations using analytics, simulation, cost modelingExperience building end-to-end IE models integrating capacity, labor, cost, PFEP, and material flow Proficiency in capacity modelling,

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Contract

Depends on Experience

Data Scientist II

Pittsburgh, Pennsylvania

•

Today

Job Description This is a hybrid role, and the successful candidate will collaborate with internal business partners to implement data-driven solutions with measurable business value. The candidate must have experience working within a team environment as well as independently, on multiple projects simultaneously, and work well within deadlines. The candidate must have experience designing, conducting, and interpreting statistical analyses using common statistical software tools (e.g., R, Pytho

Full-time

USD 88,762.00 - 110,000.00 per year

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