Career Evaluation Product — Group 1
Project Scope
| Team | AD688 Group 1 |
| Career Pathway | Business Intelligence / Data Analyst |
| Industry Code | NAICS 2022 — 523 Securities, Commodity Contracts, and Other Financial Investments and Related Activities |
| Industry Label | U.S. securities, investment and asset management |
| Data Source | WRDS jobs_2026 dataset (Module 3); MET Career Match API (Modules 1-2) |
Product Question
Which skills, locations, and role variants give an early-career candidate the strongest entry into Business Intelligence / Data Analyst roles at U.S. securities and investment firms (NAICS 523), and which skills are missing from a current student profile?
Why This Industry
Securities, commodity contracts, and other financial investment activities (NAICS 523) run continuous reporting, risk, performance-attribution, and client-analytics functions that are staffed by business intelligence and data analyst roles rather than by research scientists. Because investment analytics is shaped by regulatory reporting and legacy data infrastructure, its skill profile differs from the software sector — SQL, data governance, and domain knowledge often matter as much as machine-learning depth. The subsector is also the single largest source of analytics postings within Finance and Insurance in the data source, which makes the scope both coherent and large enough to support a decision-oriented product.
Scope Revision
Module 1 anchored the project to NAICS 5221, Depository Credit Intermediation. When pulled in Module 2, the data returned 59 postings in total, of which 13 carried salary information — too thin to support the planned skill-gap and compensation analysis later in the project. Because of that the team measured the neighbouring industry codes and moved the anchor to NAICS 523, which keeps the project inside Finance and Insurance while providing 15,176 postings, of which 6,199 matched our career pathway. Module 3 rebuilt the panel from the supplied WRDS dataset on the same scope: 24,914 postings in NAICS 523 and 7,074 after the role filter. The measurement and the filter rule are documented on the Data Preparation page.
How To Read This Site
| Page | What it provides |
|---|---|
| Project Overview | Why the product question matters and anticipated findings |
| Data Preparation | Where the data comes from, how it was filtered and cleaned, and what its limitations are |
| Exploratory Analysis | Current market analysis: volume, pay, geography, experience, flexibility, employers |
| Skill Gap Analysis | What employers ask for, measured against what the team can actually do for a realistic comparison |
| Predictive Modeling | Whether the market splits into segments a candidate can target |
| Career Strategy | Where to apply, what to learn next, how to position |
Each analysis page states how many postings it is based on, because field coverage varies across the dataset.
Team
| Name | GitHub | Contribution |
|---|---|---|
| Fuad Babaiev | fuadbbv | Data cleaning and employer normalisation; final analytics tracks; report assembly and publishing |
| Katie Bowles | kmbowles00 | Market baseline and shared visual theme; final recommendations; site requirements check |
| Jonathan Chen | jchenbu | Skill gap analysis and chart rework; interpretation of the final analytics |
Roadmap
| Module | Deliverable | Status |
|---|---|---|
| M1 | Project scope, industry selection, rationale, initial site | Complete |
| M2 | Data preparation and market baseline | Complete |
| M3 | Site restructure, enhanced exploratory analysis, skill gap analysis | Complete |
| M4 | Checkpoint — readiness review of Steps 1 to 3 | Complete |
| M5 | Final analytics, recommendations, report | Complete |
| M6 | Final presentation | Complete |