Industry
How to use BLS occupational data to forecast analytics hiring demand
A method for using BLS occupational data, including SOC lines and national projections, plus OEWS metro wage steps, release timing and a three-band dashboard.
What to take away
- Management analysts (SOC 15-2041) and operations research analysts (SOC 15-2031) are the two BLS lines to anchor an analytics hiring plan on.
- The Occupational Outlook Handbook sets national direction. OEWS tables set the metro wage and employment base you recruit against.
- BLS puts management analyst growth near 10 percent for 2023 to 2033, and data scientist growth near 36 percent.
- A metro plan needs a location quotient and a 75th percentile wage, not a national growth rate split across fifty states.
- Refresh the model when the matching BLS release lands.
The two occupation lines that carry the plan
BLS counts about 1.0 million management analysts in the United States, the largest analytics-adjacent line in the Standard Occupational Classification. The 2023 to 2033 cycle puts growth near 10 percent, with roughly 95,700 openings a year. The May 2023 OEWS vintage reported a national median annual wage near $99,400.
Data scientists sit at the far end. BLS projects about 36 percent growth over the same cycle and roughly 20,800 openings a year from a base near 210,000. Operations research analysts cover optimization and decision modeling, a smaller line with its own metro geography.
| BLS line | SOC code | National scale | Planning use |
|---|---|---|---|
| Management analysts | 15-2041 | About 1.0 million employed, 10% projected growth | Broad demand proxy for banks, insurers, hospitals, agencies |
| Operations research analysts | 15-2031 | Smaller line, quantitative modeling | Signals optimization and decision-science demand |
| Data scientists | 15-2051 | About 210,000 employed, 36% projected growth | Narrow, high-pay demand in tech and finance metros |
Every figure carries a vintage. Recheck the current release before you budget on it. It also helps to know which analytics career tracks each line feeds, because entry paths differ by line.
OEWS metro wages and the four-step model
The OEWS management analyst tables list employment counts and wage percentiles for every metro and state. The operations research analyst tables do the same for the modeling line. Estimates for May of each year publish the following April, so the vintage label always trails the calendar by roughly a year.
- Pull the occupation rows for San Jose, Austin, Boston, Seattle and New York, plus the state row where your hiring radius crosses a metro boundary.
- Divide occupation employment by total metro employment. That ratio against the national ratio is the location quotient.
- Record the median and the 75th percentile annual wage for each row.
- Multiply planned headcount by the 75th percentile wage. Budget against the top quartile, not the midpoint.
Four Steps to a Metro Demand Model
- Pull management analyst and data scientist rows
- Divide employment by total metro employment
- Pull median and 75th percentile wages
- Multiply headcount by 75th percentile wage
A location quotient above 1.0 means the metro already carries more of that occupation than the national average, so recruiters and competing employers are present. Below 1.0 you are building the market rather than joining it, and time to fill stretches.
Metro wage values are not printed here. They change with each vintage, and a stale number misleads more than a blank cell. Read them off the table and keep the vintage stamp inside the model.
Austin and Seattle pull from different industry mixes, so one pay band rarely serves both, which is the case the Austin and Seattle pay comparison sets out.
Example: a twenty-hire plan across three metros
Suppose a national plan calls for twenty analytics hires. In the first metro, management analysts show a location quotient of 1.4 and data scientists 0.8.
That mix supports twelve management analyst hires at or near market rate, with eight data scientist slots carrying a longer time to fill and a higher band. Run the exercise for each metro and compare. The comparison is the plan, and a forecasting checklist keeps the inputs fixed between releases.
The BLS release calendar
The BLS release schedule sets the refresh calendar. A forecast updated in the wrong week argues with itself.
- monthly, usually the first Friday. It sets the hiring tone, not the occupation detail.
- annual, published the April after the May reference period. Recompute wages and location quotients.
- multi-year cycle. Recheck national growth assumptions.
- continuous. Assign one analyst to read it and write a short note.
BLS Release Refresh Calendar
- First Friday monthlyEmployment Situation headline
- AnnuallyOEWS metro wage tables
- Every two yearsOccupational Outlook Handbook projections
- ContinuouslyMonthly Labor Review reading task
Freeze the plan between releases. One revised weekly is a reaction, not a plan.
A demand dashboard with three bands
Keep the page to national, metro and internal evidence.
- Nationalthe OOH projection for each line, plus the latest Employment Situation headline.
- MetroOEWS employment, location quotient, median wage and 75th percentile wage for each target metro.
- Internalrequisitions opened, offers accepted, time to fill, and the gap against the metro 75th percentile band.
Fewer tiles beat more. Each tile needs a named source and a date stamp, so a reader can see why a number moved. When the rebuild scope grows, dashboard cost per user benchmarks size the spend before the finance review.
