WhyHow.AI

### **The Mission**

Turn messy public-record & web data into trustworthy mass-tort signals - via rapid EDA, rigorous modeling, and production-grade APIs - so lawyers act on facts, not hunches.

### **What You’ll Own**

*   **EDA Autopilot -** Grab raw JSON/CSV/HTML, profile it, spot outliers, and surface “aha” patterns - without waiting for a PM to ask.
*   **Model Builder** \- Train and tune classification / ranking models (Typical classifiers, light ML, LLM-based RAG) that lift recall & precision week-over-week.
*   **API Integrator** - Package models behind FastAPI endpoints, validate / marshal schemas with Pydantic, and push to GitHub Actions CI in a day.
*   **MLOps Wrangler** - Monitor drift, schedule batch recall and write lightweight tests.
*   **Insight Storyteller** - Ship clear notebooks / dashboards & concise Loom walk-throughs that legal SMEs grok in minutes.
*   **Startup Swiss-Army Knife** - Spot gaps (data gaps, labeling gaps, infra gaps) and patch them before anyone asks. Ambiguity is the default.  
    

### **What Success Looks Like**

*   Week 4 - First exploratory notebook flags a recall-worthy defect the founders didn’t see.
*   Week 6 - A FastAPI route serving the trained model hits < 300 ms P95 latency in prod.
*   Quarter 1 - Recall ↑ 15 pp and false-positive rate ↓ 10 pp on live web-scrape feed; zero pager-alerts.

### **Your Toolkit**

*   2-4 yrs Python data science: pandas/Polars, Astral stack, PyTorch/TF.
*   Comfort spinning up FastAPI + Pydantic micro-services.
*   Familiarity with using rate-limited LLMs to augment and clean existing datasets
*   Solid SQL & object-storage chops (Postgres, DuckDB, S3).
*   CI/CD familiarity (GitHub Actions or similar); basic IaC a plus.
*   You document & demo your work proactively - no babysitting required.

### **Nice-to-Haves**

*   Prior scraping work (Scrapy, Playwright) or PACER/NHTSA/FDA datasets.
*   Experience with vector DBs (Qdrant, pgvector) & prompt-engineering.
*   Exposure to SOC 2 or other regulated-data environments.

### **Interview Process**

*   15 minute initial overview call
*   ~2-3 hour take home assessment with dataset provided. EDA and written communication expected
*   1 hour pair programming assessment with CTO, with screenshare and agent use
*   30 minute Q&A with founding team  
    

### **Why Join**

Green-field ML canvas, instant customer feedback, and exposure to leadership in a venture-backed startup already generating revenue and real impact in legal tech. Shape how mass-tort intelligence is built in the AI era.

Ready to build? Apply today.

### **Interview Process**

*   15 minute initial overview call
*   ~2-3 hour take home assessment with dataset provided. EDA and written communication expected
*   1 hour pair programming assessment with CTO, with screenshare and agent use
*   30 minute Q&A with founding team