Overview & Program Purpose
Welcome to the Wharton AI & Analytics Accelerator! The Accelerator bridges top-tier student talent with industry partners to solve pressing business challenges through data-driven insights, descriptive/predictive analytics, and artificial intelligence.
Program Benefits
For Corporate Partners
Deep engagement with dedicated student teams to produce actionable insights, vetted solutions guided by Wharton faculty and Senior Fellows, and direct engagement with top Penn/Wharton talent.
For Students
Hands-on experience as data translators and decision-makers, opportunity to work with real-world enterprise datasets, direct mentorship from industry leaders and faculty, and high-visibility professional networking.
Team Structure & Roles Directory
Successful Accelerator engagements rely on clear governance and accountability across both company stakeholders and student execution teams.
Corporate Partner Roles
Project Champion
High-level executive sponsor who initiates the partnership, assists in shaping business questions, and aligns company stakeholders.
Project Lead
Main day-to-day corporate contact. Develops achievable business scope pre-project; ensures team trajectory aligns with business goals; attends weekly check-ins; acts as liaison to internal stakeholders; oversees post-project implementation.
Data Specialist / Data Scientist
Technical resource responsible for securely pulling and provisioning data access to Wharton teams, assisting with technical scope, and integrating final models into company environments post-project.
Student Team Roles
Business Engagement Lead (BEL)
Overall student project lead and primary business liaison to WAIAI and the corporate partner. Coordinates team workflow, manages timelines, and owns the final presentation deck and executive summary deliverables.
Technical Engagement Lead (TEL)
Technical student lead. Directs descriptive/predictive modeling strategies, determines machine learning approaches, and oversees delivery of modular code and visualizations.
Senior Analyst
Technical anchor leading heavy coding implementation, advising junior analysts, and executing complex analytics routines.
Junior Analyst
Supporting analyst executing coding tasks, data cleaning, feature engineering, and predictive modeling applications under senior guidance.
Program Support Roles
Technical Assistant (TA)
Dedicated Wharton TA supporting the student team on complex technical challenges, model selection, code optimization, and data infrastructure access.
Wharton Mentor (Faculty / Industry)
Guides team strategy, maintains focus on high-level business impact, attends weekly check-ins, and supports engagement leads weekly.
WAIAI Staff Liaison
Program administrator who attends weekly check-ins to monitor project health, ensure milestone delivery, assist with scheduling, and oversee operations.
Project Lifecycle & Milestone Roadmap
The Accelerator follows a structured 9-week cadence. (Note: Timeline may be adjusted dynamically for specialized AI tracks).
Phase 0: Pre-Project & Onboarding
Corporate Onboarding
Execute Sponsored Research Agreements, finalize business questions, provision anonymized data access to Wharton, and brief internal HR/Comms teams for talent sourcing and brand alignment.
September 30 | Internal Alignment
Internal Wharton meeting with BEL, TEL, Mentor, TA, and WAIAI Staff Liaison. Confirm scheduling for the final Summit.
Phase 1: Ingestion & Exploratory Analysis (Weeks 1–4)
October 7 | Kickoff
Joint kickoff meeting hosted by company leads. Company overview presentation, tool/data access confirmation, establish weekly check-in schedule, and optional Exploratory Data Analysis (EDA) homework assignment issuance.
October 14 | Data Wrangling
Students perform data cleaning, reformatting, and initial descriptive analysis/visualizations.
October 21 – October 28 | EDA Deep-Dive
Deep descriptive analysis to validate data hypotheses and prep for modeling strategies. Schedule Midpoint meeting.
Phase 2: Midpoint & Predictive Modeling (Weeks 5–6)
November 4 | Midpoint Check-in
Formal presentation to corporate leadership, Wharton mentors, and staff. Presentation of preliminary insights and strategic roadmap for final weeks.
November 11 | Modeling & Testing
Predictive model construction, feature engineering, cross-validation, and alignment of technical outputs to actionable business decisions.
Phase 3: Refinement, Summit & Offboarding (Weeks 7–9+)
November 18 | Final Polish
Model refinement, deck generation, and rigorous audit to ensure all slides are properly titled, charts and models are are labeled, and prominent call-out boxes are added.
December 4 | AI & Analytics Summit
Submit final slides to partner 24 hours prior. Private company presentation & Summit on Wharton Campus (Friday, Dec 4).
December 9 | Debrief & Transfer
Full code handover, delivery of Executive Summary and visual decks, and WAIAI debrief meeting to evaluate program outcomes.
Post-Project
Model integration into company production environments, student LinkedIn endorsements/networking, case study development, and 6–12 month follow-up with Wharton to track long-term ROI.
Best Practices & Collaboration Framework
To ensure frictionless collaboration between student teams and corporate sponsors, adhere to these guidelines:
Scope Management
Keep Scope Achievable
Focus on what can be realistically executed within 8 working weeks. Avoid ‘scope creep’ once the modeling strategy is set in Week 3.
Core Questions to Answer Before Starting
- Can the business questions be definitively answered with the provided dataset?
- Why does this problem matter to current business strategy?
- What does a successful deliverable look like in practice?
Communication Protocols
Cadence & Attendance
Attend mandatory 30–60 minute weekly check-ins (Company Project Lead, BEL, TEL, Mentor, TA, WAIAI Liaison).
Flexibility & Professionalism
Student teams often run computational workflows during evenings and weekends; maintain open communication channels.
Virtual & In-Person Engagement
Turn cameras on during remote check-ins. Sponsors are strongly encouraged to visit the team on Wharton’s campus at least once during the term.
Approved Infrastructure
Communication through Slack, Microsoft Teams, or Email. File exchange via Wharton-managed Box or partner secure storage environments.
Questions? Contact Us
