Variance analysis jobs in 2026 — demand, top roles hiring, and related skills

As of 2026-11-05, Variance analysis appears in 356 job postings indexed by Skillenai over the past 90 days — Program Manager has the most postings mentioning Variance analysis, with demand share down 0.2% vs the prior 4 weeks.

Last updated · 90d ending 2026-11-05

Postings · last 90 days
356
Demand vs prior month
down 0.2% vs the prior 4 weeks
Top role · 10.7% of skill postings
Top hiring metro
San Francisco

Which roles want Variance analysis?

Upload your resume and Skillenai will show which roles your Variance analysis experience fits, which skills you already cover, and what is missing.

Frequently asked questions about Variance analysis

+Is Variance analysis in demand in 2026?

Yes. Variance analysis appears in 356 job postings indexed by Skillenai over the 90 days ending 2026-11-05. Program Manager accounts for the most postings mentioning Variance analysis (10.7% of all postings mentioning Variance analysis).

+What jobs require Variance analysis?

According to the Skillenai jobs index over the 90 days ending 2026-11-05, among roles with at least 20 postings, the highest shares mentioning Variance analysis are FP&A Manager (50.0% of that role’s postings mention Variance analysis), FP&A Analyst (46.5% of that role’s postings mention Variance analysis), Finance Analyst (40.7% of that role’s postings mention Variance analysis).

+What skills are commonly paired with Variance analysis?

Across job postings indexed by Skillenai (90 days ending 2026-11-05), Variance analysis most often appears alongside forecasting, budgeting, SQL, Power BI, financial modeling.

+Where is Variance analysis most in demand?

As of 2026-11-05, the metro areas posting the most jobs requiring Variance analysis are San Francisco, New York City, London, Palo Alto, Bengaluru, according to the Skillenai jobs index.

+How can I keep up with new Variance analysis content and jobs?

Skillenai indexes news, blog posts, and research papers mentioning Variance analysis alongside the jobs index. You can subscribe to a daily email digest of new Variance analysis content from your Skillenai account.

+Which skills come before and after Variance analysis?

The skill-flow chart shows skills documented in adjacent positions across observed employer changes. An outgoing skill is documented in the following position but not the preceding one. These are ideas to explore, not proven prerequisites, acquisition dates, or levels of mastery. Each ribbon counts employer moves with that skill pair; one move can contribute several pairs.

Weekly indexed postings requiring Variance analysis — last 90 days

Salary distribution

Box = 25th–75th percentile · tick = median · whisker = 10th–90th · USD, annualized

Career paths around Variance analysis

Skills documented before and after this skill across employer changes.

Historical career profiles · all locations

Skills before Variance analysis

Before Variance analysisfinancial modeling → Variance analysis: 4 observed employer moves with this skill pairsql → Variance analysis: 4 observed employer moves with this skill pairTableau → Variance analysis: 4 observed employer moves with this skill pairpython → Variance analysis: 3 observed employer moves with this skill pairExcel → Variance analysis: 3 observed employer moves with this skill pairPower BI → Variance analysis: 2 observed employer moves with this skill pairAgile → Variance analysis: 2 observed employer moves with this skill pairreports → Variance analysis: 2 observed employer moves with this skill pairVarianceanalysisfinancial modeling: 4 movesfinancial modeling4 movessql: 4 movessql4 movesTableau: 4 movesTableau4 movespython: 3 movespython3 movesExcel: 3 movesExcel3 movesPower BI: 2 movesPower BI2 movesAgile: 2 movesAgile2 movesreports: 2 movesreports2 moves

Skills after Variance analysis

After Variance analysisVariance analysis → python: 4 observed employer moves with this skill pairVariance analysis → data analysis: 2 observed employer moves with this skill pairVariance analysis → Data Extraction: 2 observed employer moves with this skill pairVariance analysis → Journal entries: 2 observed employer moves with this skill pairVariance analysis → scikit-learn: 1 observed employer moves with this skill pairVariance analysis → data quality checks: 1 observed employer moves with this skill pairVariance analysis → cost estimating: 1 observed employer moves with this skill pairVariance analysis → Time Creation: 1 observed employer moves with this skill pairVarianceanalysispython: 4 movespython4 movesdata analysis: 2 movesdata analysis2 movesData Extraction: 2 movesData Extraction2 movesJournal entries: 2 movesJournal entries2 movesscikit-learn: 1 movesscikit-learn1 movesdata quality checks: 1 movesdata qualitychecks1 movescost estimating: 1 movescost estimating1 movesTime Creation: 1 movesTime Creation1 moves
How to read this chart · view counts

Each side is an independent set of observed employer moves, not the same people followed through three stages. Ribbon widths compare move counts within that side. Internal moves are not included.

The following position documents a skill that the preceding position does not. Skills must be linked to both positions, with clear dates and no overlap. One move can connect several skill pairs. These patterns suggest skills to explore; they do not establish prerequisites, when a skill was learned, or a higher skill level.

Source: Skillenai talent graph, historical career profiles. Historical descriptions and coverage can change. Only the leading published connections are shown.

Observed connections and move counts
ConnectionMoves
Before: financial modeling4
Before: sql4
Before: Tableau4
Before: python3
Before: Excel3
Before: Power BI2
Before: Agile2
Before: reports2
After: python4
After: data analysis2
After: Data Extraction2
After: Journal entries2
After: scikit-learn1
After: data quality checks1
After: cost estimating1
After: Time Creation1

Roles most likely to require Variance analysis

Among roles with at least 20 postings in the same period.

RolePostings mentioning skill% of role postings mentioning skill
FP&A Manager1950.0%
FP&A Analyst2046.5%
Finance Analyst1140.7%
Financial Analyst1619.0%
Finance Data Analyst414.8%
Product Data Scientist25.4%
Actuarial Analyst14.5%
Finance Manager14.2%
Business Operations Manager13.8%
Senior Data Engineer13.6%

Roles with the most Variance analysis postings

RolePostings mentioning skillShare of skill postings
Program Manager3810.7%
Business Analyst287.9%
FP&A Analyst205.6%
FP&A Manager195.3%
Financial Analyst164.5%
Data Analyst154.2%
Finance Analyst113.1%
Finance Business Partner61.7%
Systems Engineer61.7%
Data Scientist41.1%

Top companies posting jobs requiring Variance analysis

Employers ranked by indexed job postings in the last 90 days.

Top companies posting jobs requiring Variance analysis
CompanyPostings · 90 days
Globalpr7
CLERA5
Northrop Grumman5
AstraZeneca5
Anthropic4
Spacelift4
Amazon3
Google3
Medtronic3
Humana3

Job postings indexed over the past 90 days, grouped by resolved employer. Counts are postings, not hires. Companies without a published page appear without a link.

Top metros hiring for Variance analysis

NamePostingsShare
San Francisco123.4%
New York City113.1%
London51.4%
Palo Alto51.4%
Bengaluru30.8%
Boston30.8%
Las Vegas30.8%
Taipei30.8%
Toronto30.8%

Skills commonly paired with Variance analysis

Explore related pages

How this was computed

Weekly job-count charts omit the incomplete current week (weeks start Monday, UTC). Totals include all postings in the stated window. Counts derive from the Skillenai jobs index over the 90 days ending 2026-11-05. Skills are resolved against the Skillenai canonical taxonomy, so the same entity is counted whether a posting writes 'Python', 'Python 3', or 'python'. Role prevalence divides postings mentioning Variance analysis by all postings for each role in the same window, ranking roles with at least 20 postings. Role distribution divides each role’s Variance analysis postings by all Variance analysis postings, including postings without a role. Shares need not sum to 100% for the displayed roles. Pages refresh weekly (or daily for the top-50 most-requested skills). Adjusted posting share: 0.1% to 0.1%. Demand share change is the relative percentage change between these adjusted shares. Each employer-and-ATS group has at least 10 postings in each 90-day window; its earlier posting count supplies the same weight in both windows. The panel includes 2,472 identified employers and covers 71% of earlier and 70% of latest indexed postings. Windows: 2026-07-07 to 2026-10-05 and 2026-08-04 to 2026-11-02 (UTC; end dates excluded). The windows overlap by 62 days. Dates reflect indexing, not the employer’s posting date. This measures posting mix, not total hiring or market-wide demand. Matching excludes entrants and exits; changes in crawl completeness within an employer or ATS can still affect the result.

source
Skillenai jobs index, deduplicated daily
entity_id
558f077691a4e856
data_as_of
2026-11-05
window_days
90
Compiled by Jared Rand · Data sourced from the Skillenai labor market index