Data and dashboards
Analysis, visualization, KPI summaries, forecasting, Power BI dashboards, and decision-support tools.
A paid Northeastern experiential partnership
Move real projects forward.
Working Labs are paid, project-based partnerships that pair your organization with interdisciplinary Northeastern graduate student teams and expert faculty advisors to address real business challenges in AI, data analytics, software development, research, and operations.
TALENT
OUTPUTS
1
Add capacityMove work forward without creating a full-time role.
2
Test ideasExplore an AI, data, product, operations, or research question before making a larger investment.
3
Build pipelineSee how emerging Northeastern talent scopes problems, collaborates with your team, and delivers usable work before you hire.
What it is
Paid project support that doubles as a talent strategy.
Invest in a Working Lab when you have a meaningful project that needs focused capacity, specialized skills, and a structured path forward.
Through a paid engagement with Northeastern, graduate students deliver actionable insights and defined project outputs tailored to your organization’s goals. Faculty and expert advisors provide guidance, while Northeastern recruits and manages the student team and oversees the engagement.
AI workflow exploration
Use case research, automation mapping, model comparison, recommendation engines, and applied AI concepts.
Product research
User research, prototype support, market scans, technology scans, and early product validation.
Process improvement
Workflow mapping, requirements gathering, operations planning, and next-step recommendations.
Technical concepts
Proofs of concept, web applications, internal tools, data pipelines, and cloud-enabled solutions.
Research and trends
Market, technology, literature, demographic, and business-context scans that help teams decide where to focus.
How it works
Scope. Match. Work. Deliver.
Through a paid partnership, Northeastern helps shape the project, match the right team, manage the engagement, and support students as they move toward usable deliverables.
1
Scope
Identify and shape a suitable project with Northeastern faculty and staff.
2
Match
Build a student team with the right mix of skills and domain interests.
3
Work
Move against milestones through progress updates, partner feedback, and faculty guidance.
4
Deliver
Receive usable outputs like prototypes, models, dashboards, technical concepts, and strategic insights.
A paid, project-based consulting engagement built around a defined business challenge. Your organization funds the engagement based on its scope, timeline, and team requirements. Northeastern recruits, manages, and pays the students, with faculty or expert advisors helping guide the team toward high-quality outputs. Project scope, team composition, timeline, deliverables, and investment are established with your organization before work begins.
AI and analyticsSoftware developmentResearchOperations
For organizations ready to move beyond a stand-alone project, an expanded paid partnership can include defined annual faculty capacity, dedicated FTE student talent, and an embedded space commitment.
Teams are built around the need. A custom AI tool, for example, could include a project manager, back-end developer, AI engineer, and Northeastern faculty advisor.
Why Northeastern
Students trained for real employer needs.
- Experiential learning is Northeastern’s model.
- Teams bring skills across AI, data, technology, product, research, and business.
- Faculty and expert guidance add structure, oversight, and domain perspective.
- A global university network connects employers with talent, research, and campuses across industries.
Best-fit prompts
A good project usually starts with one of these.
- “We need to explore this.”
AI use cases, market research, product discovery, workflow mapping. - “We need to make sense of this.”
Data analysis, dashboards, customer insights, operational trends. - “We need to test this.”
Prototypes, internal tools, proofs of concept, process improvements.
Project examples
Real work, real outputs.
Working Labs have supported sustainability analysis, predictive tools, recommendation engines, customer retention applications, KPI dashboards, and demand forecasting.
Sustainability analysis tools
Faculty: 1Students: 2
Students developed data tools to enhance sustainability analysis in retail and optimize return from mineral resource recovery.
Predictive decision-support tool
Faculty: 1Students: 2
Built a scalable, predictive tool integrated with the local data environment to help guide decision making alongside internal subject matter experts.
AI recommendation engines
Faculty: 4Students: 5
Faculty and student teams are building AI recommendation engines to improve customer service, marketing, and new product deployment. The first engine improved new product conversion 2X.
Customer retention web application
Faculty: 1Students: 66 months
Students researched internal business requirements and designed and developed a web-based application for customer retention.
Automated KPI dashboard
Faculty: 1Students: 515-20 weeks
Students designed and built an automated, scalable web-based dashboard to display and track identified KPIs.
Demand forecasting and operations planning
Craft beer130 employees
Students compared forecasting methods, evaluated product-level demand, and delivered code, forecasts, and dashboarding to support sales and brewery operations planning.
Featured case study
Allagash used real data to sharpen forecasting.
Allagash Brewing Company wanted to improve weekly sales forecasting by SKU, optimize inventory and shipping for seasonal brands, and understand which features mattered most for predicting sales.
1.6M+real-world records across depletions, shipments, inventory, and product attributes
6forecasting approaches compared before selecting best-fit methods by SKU
78weeks of forward-looking forecasts delivered for representative products
16%modeled cost savings when forecasting improvements were paired with smarter inventory and shipping logic
Students built and compared Prophet, Holt-Winters Exponential Smoothing, S-ARIMA, and naive baselines. Each SKU was assigned the method that minimized Weighted Absolute Percentage Error.
Allagash White was steadier and easier to forecast, while seasonal beers had shorter selling windows and less historical data. Seasonal SKUs improved from a 62% MAPE baseline to 44.5% WAPE in the project summary.
The team delivered Python forecasting code, a CSV of 78-week forward forecasts for 19 SKUs, and a Power BI dashboard filterable by date, state, product, and forecast versus actuals.
Weather-enriched modeling measurably sharpened forecast accuracy. Demographic data was too static for week-to-week forecasting, and news sentiment added more noise than signal.
“The students at the Roux Institute took a specific business opportunity and provided different models to help us reach our goals. Their varying backgrounds and skills yielded several approaches, each helping us understand the opportunity in a new way, ultimately putting us in a better position to capitalize on the opportunity.”
Allagash Brewing Company
Team structure
Built around your need.
A Working Lab team can combine project management, technical build capacity, AI expertise, and faculty advising.
Sample objective
Custom AI tool for project estimating and sales
Project ManagerMS Project Management
Back-end DeveloperMS Computer Science
AI EngineerMS Artificial Intelligence
NU Faculty AdvisorStructure, oversight, and domain perspective
Ready to start?
Have a project sitting on the someday list?
Talk to our team about your project, desired outcomes, timeline, and available budget to determine whether a paid Working Lab partnership is the right fit.