Overview
What this challenge is about.
Train a Node2Vec model on a B2B account graph, recommend top-5 expansion targets, and backtest conversions. Get a verifiable certificate.
The scenario
The scale-up (around 200 people, around USD 35 million ARR, North America-focused) currently runs account-expansion by hand-curated lists; conversion-rate uplift of even 1.5x would justify productionizing the model.
The Brief
What you'll do, and what you'll demonstrate.
Build an account-similarity link-prediction model that meaningfully improves account-expansion conversion rate over hand-curation.
Earning criteria — what you'll demonstrate
- Construct an account graph from tabular CRM-like data
- Train and evaluate node embeddings for link prediction
- Backtest a recommender against historical conversions
- Communicate model uplift to a sales operations audience
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Aligned coursework coming soon.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
- Link Prediction
Apply link prediction to solve real industry problems and demonstrate production-level capability.
- Node Embeddings
Apply node embeddings to solve real industry problems and demonstrate production-level capability.
- Node2vec
Apply node2vec to solve real industry problems and demonstrate production-level capability.
- Graph Construction
Apply graph construction to solve real industry problems and demonstrate production-level capability.
- Evaluation
Apply evaluation to solve real industry problems and demonstrate production-level capability.
- Pytorch Geometric
Apply pytorch geometric to solve real industry problems and demonstrate production-level capability.
Careers
Career paths this challenge builds toward
Completing this challenge demonstrates skills that transfer directly to these roles:
Data Scientist
Shipping a link-prediction recommender backed by a time-aware backtest and a sales-ops memo is exactly the day-one work of a data scientist in B2B SaaS.
This challenge sharpens
- link-prediction
- node-embeddings
- evaluation
Machine Learning Engineer
Building a reproducible graph + embedding pipeline that the sales team can re-score against is core MLE work for go-to-market data platforms.
This challenge sharpens
- pytorch-geometric
- graph-construction
- node-embeddings
Applied AI Scientist
Translating model metrics into a sales-ops recommendation pilot is the applied-AI-scientist craft of putting ML into operations.
This challenge sharpens
- link-prediction
- evaluation
- node2vec