MinHash Similarity Sketch for a Job-Board Deduplication Pipeline
Overview
What this challenge is about.
MinHash Similarity Sketch for a Job-Board Deduplication Pipeline. Advanced challenge in code. Writing production code that solves real engineering problems, ...
The Brief
What you'll do, and what you'll demonstrate.
Build a MinHash + LSH sketch that finds near-duplicate job postings at a Jaccard threshold of 0.7 with at least 95 percent recall and at least 85 percent precision.
This is not a coding exercise. It is the work a software engineer does between a Jira ticket and a merged PR. That distinction matters to every hiring manager who has seen candidates solve LeetCode problems and none who have shipped production code under real constraints.
When you finish, you will have something most graduates do not: a real-world deliverable, verified by Ewance, that you can show to a hiring manager and say "I did this. Here is the proof."
Earning criteria — what you'll demonstrate
- Derive the relationship between LSH bands (b), rows per band (r), and the S-curve probability of collision
- Implement MinHash with permutation-based hashing without leaking bias
- Measure approximate-vs-exact deduplication quality with labeled data
- Reason about the recall/precision trade controlled by b and r
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Randomized Algorithms
Master · Algorithms
Strong alignment
This challenge maps to Randomized Algorithms at the Master level. It sharpens the same practical skills your coursework expects — but in a real industry context with actual constraints and deliverables.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
- Minhash
Apply minhash to solve real industry problems and demonstrate production-level capability.
- Locality Sensitive Hashing
Apply locality sensitive hashing to solve real industry problems and demonstrate production-level capability.
- Probabilistic Data Structures
Apply probabilistic data structures to solve real industry problems and demonstrate production-level capability.
- Python Programming
Apply python programming to solve real industry problems and demonstrate production-level capability.
- Evaluation Metrics
Apply evaluation metrics to solve real industry problems and demonstrate production-level capability.
- Text Processing
Apply text processing 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: