Data Scientist Salary in Minneapolis — 2026 BLS Data
Salary distribution
Percentile breakdown of Data Scientist base salaries in Minneapolis.
BLS OEWS May 2024 data for the Minneapolis-St. Paul-Bloomington metro area places the median data scientist base salary at $119,860 (SOC 15-2051). That figure is 6.5% above the national median of $112,590 for the same occupation code — a modest but real premium that reflects the concentration of large, analytically sophisticated employers in a market most coastal job boards still undercount. The headline number, though, obscures a $81,340 spread from P25 to P90, an industry mix that splits the market into two distinct pay bands, and a cost-of-living picture that makes that $119,860 buy more than it looks like on paper.
What the $119,860 median actually hides
The Minneapolis metro percentile distribution runs from $60,070 at the 10th percentile to $171,260 at the 90th. The P25-to-P75 band — $89,920 to $144,200 — is where the majority of working data scientists in the metro actually sit. That $54,280 spread within a single occupation code in a single metro area is not measurement noise; it reflects genuine structural differences in who is hiring, for what kind of work, and at what level.
At the bottom of the distribution, you find roles that carry the data scientist title but are primarily reporting and SQL-based analytics work — what used to be called “business intelligence” before the title inflation of the 2010s arrived. At UnitedHealth Group’s subsidiary Optum, for instance, Indeed-reported Minneapolis data scientist pay runs toward the lower end of the market, with some reported figures in the $85K-$100K range, reflecting the high volume of entry-level analytics roles within that organization. These roles are real data science jobs; they’re just not the same job as a machine learning engineer building recommendation systems at Target’s AI lab.
At the top, you have senior and staff-level applied scientists and ML engineers at Target’s technology headquarters, Best Buy, and the growing financial services cluster — Ameriprise Financial, U.S. Bancorp, and Piper Sandler among them. A senior data scientist with seven or eight years of experience and a production ML portfolio at Target or a Minneapolis fintech can realistically reach the $144K-$165K range on base alone.
The median also flattens level differences. Entry-level data scientists in Minneapolis — typically MS graduates in statistics, computer science, or data science — start in the $85K-$100K range. Mid-level ICs with three to five years of experience land in the $110K-$135K corridor. Senior-level roles (typically 6+ years, owning a project domain end-to-end) sit in the $140K-$165K band. The BLS survey collapses all three into SOC 15-2051 and reports a single median. Knowing which rung a specific open role actually corresponds to is more useful than any single summary statistic.
Minneapolis versus comparable data science hubs
Minneapolis is not Chicago, Seattle, or the Bay Area — and for many data scientists, that’s a deliberate choice rather than a consolation prize.
Chicago’s metro data scientist median runs approximately $125K-$135K on base, driven up by a substantial quantitative finance cluster (Citadel, DRW, Morningstar) that pays $150K-$200K+ base for numerically sophisticated roles that would show up in the same SOC code. Strip out the quant-finance premium and Chicago’s generalist data science market is closely matched to Minneapolis. Seattle anchors higher, around $145K-$165K median base, but that number is heavily weighted by Amazon and Microsoft roles that include meaningful equity — which the BLS base figure doesn’t capture. On a pure base-salary comparison, Minneapolis runs roughly 15-25% below Seattle.
San Francisco median base for data scientists sits in the $165K-$185K range in comparable 2024-2025 survey data — a 38-54% premium over Minneapolis. On total compensation the gap is wider, because SF tech equity is a much larger fraction of the package. The catch is San Francisco’s cost-of-living index of 178.6 versus Minneapolis’s 103. After COL adjustment, the SF premium in purchasing-power terms compresses to roughly 25-30% — still real, but substantially smaller than the raw number suggests.
Austin ($115K-$125K median base) is within a narrow band of Minneapolis on raw salary. Denver ($108K-$118K) runs slightly below. Both cities have lower cost-of-living indices than Minneapolis, so the purchasing-power differences are modest either way. The practical argument for Minneapolis over those markets is employer stability: Target, UnitedHealth, and 3M are Fortune 500 anchors with decades-long data science hiring records, whereas Austin and Denver’s data science markets are more dependent on growth-stage tech companies that shed headcount aggressively when funding tightens.
What drives the spread: company tier, industry, and specialty
Industry mix is the dominant factor. Minneapolis has two dominant demand pools for data scientists: healthcare/insurance and retail/consumer. They pay differently and reward different skills.
The healthcare and insurance cluster — UnitedHealth Group (the second-largest employer in Minnesota by revenue), Optum, Allscripts, Medica, and several large health system analytics teams — absorbs a large volume of data scientists at the $85K-$120K base range. These roles often emphasize SQL, Python, statistical modeling for outcomes prediction, and regulatory-compliant modeling practices. The work is genuinely complex; healthcare claims data is some of the messiest real-world tabular data that exists. The pay is not aggressive by tech standards, but the job security, benefits, and scope of impact (population health modeling, fraud detection, utilization management) are real compensations that don’t show up in base salary benchmarks.
The retail and consumer analytics cluster — Target’s Minneapolis headquarters is one of the largest concentrations of retail data science talent outside of Bentonville, Arkansas — pays more aggressively and expects more on the ML engineering spectrum. A data scientist on Target’s personalization or demand forecasting team is typically expected to build and ship production models, not just generate analyses. Accordingly, base salaries for these roles trend toward the P75-P90 range of the distribution ($144K-$165K+) when the role is genuinely senior.
Specialty premium follows national trends. Machine learning engineering, MLOps, and large language model fine-tuning roles command 20-30% premiums over generalist data science at equivalent experience levels. Natural language processing and recommendation systems skills are particularly valued in Minneapolis’s retail and media (Univision, Sinclair, Gray Television have analytics operations in the metro) sectors. Conversely, roles that blend data science with the title but operate primarily in Tableau and Excel at a mid-size manufacturer or distributor are often priced at or below the P25 threshold of $89,920.
Company stage matters less here than in coastal markets. Minneapolis has relatively few venture-backed startups compared to the Bay Area or New York. The data science job market is dominated by large, publicly traded corporations and a handful of well-capitalized growth-stage companies. That means equity is a smaller part of the compensation story than in SF or Seattle — there are fewer $50K RSU grants alongside modest bases — but also fewer lottery-ticket option packages that never vest into anything. The tradeoff is predictability over upside.
Total compensation breakdown
BLS captures base wages only. For a mid-level Minneapolis data scientist targeting the P50-P75 band — roughly three to six years of experience, running models in production, working at a major corporate employer — the full compensation picture looks like this:
- Base salary: $119,860. This is the BLS-reported figure and what appears on your offer letter as the foundational number. At most Minneapolis employers (healthcare payers, Fortune 500 retailers, financial services), base is the stable majority of cash compensation and the number that determines your 401(k) match, ESPP eligibility, and benefits elections.
- Annual cash bonus: ~$12,000. Healthcare and financial services companies in Minneapolis typically pay 8-12% of base as a performance bonus, formula-driven and paid reliably when the organization hits plan. Target famously pays a variable bonus tied to company performance that has historically paid out near its target; Ameriprise and U.S. Bancorp operate similar structures. Growth-stage tech companies sometimes replace the bonus structure with a higher base or front-load equity instead.
- Equity (annualized): ~$15,000. This is the most variable element and also the most market-specific one. At a public retailer like Target or Best Buy, mid-level data scientists receive modest RSU grants — typically $40K-$60K over a four-year vest, or roughly $10K-$15K annualized. At financial services firms (Ameriprise, Piper Sandler), equity may be minimal or structured as deferred compensation below the VP level. The handful of growth-stage tech companies in the market (several fintech and healthtech players have Minneapolis offices) offer larger option packages but with the typical pre-IPO uncertainty attached. The $15K figure is a realistic average across the full employer mix; your specific situation could range from $0 (insurance, traditional finance) to $35K+ (public tech or late-stage startup).
That sums to roughly $147,000 total compensation for a mid-level Minneapolis data scientist — modestly above what BLS base alone would suggest, reflecting a market where cash compensation is the dominant form and equity plays a supporting rather than starring role. Senior data scientists at the P75-P90 range, factoring in stronger bonus targets and larger equity grants at top employers, are looking at $175K-$215K total compensation.
Cost-of-living adjusted picture
Minneapolis’s cost-of-living index of 103 means the metro runs almost exactly at the US average — a number that consistently surprises people who associate the upper Midwest with cheap living. Housing in Minneapolis runs slightly below the national average (the metro median home price was approximately $340,000 in 2024 according to Minnesota Realtors data), but healthcare costs run meaningfully above average, and Minnesota’s income tax structure — rates up to 9.85% for high earners, with the top bracket beginning at $183,340 for single filers — reduces take-home pay more than most states.
On purchasing-power grounds: a $119,860 Minneapolis base has approximately the same purchasing power as $116,369 at the national average price level. That equivalence makes Minneapolis one of the better places in the country to be a data scientist purely on a COL-adjusted basis. Consider the comparison chain:
- San Francisco ($165K-$185K median base, COL 178.6): COL-adjusted, a $175K SF salary buys the equivalent of about $98,000 in national-average purchasing power. A Minneapolis data scientist at $120K base is actually ahead in real terms.
- Chicago ($125K-$135K median base, COL ~116): The COL premium over Minneapolis is modest — Chicago costs roughly 13% more. On purchasing power, the cities are closely matched; Chicago’s base premium is mostly consumed by higher housing and transportation costs.
- Austin ($115K-$125K median base, COL ~112): Austin runs slightly cheaper than Minneapolis but salaries also run slightly lower. Net of cost differences, the purchasing-power difference is small enough that other factors — employer quality, career optionality, personal preference — should drive the decision.
What Minneapolis offers distinctly on COL grounds is the combination of near-national-average prices with above-average employer scale. A $140K senior data scientist salary at a Minneapolis Fortune 500 leaves substantially more discretionary income than a $165K salary in Boston, Chicago, or Denver once housing, state taxes, and cost differentials are factored in. That’s not a reason to anchor low in negotiations — but it is a reason not to feel underpaid relative to coastal peers who appear to earn more on paper.
Three-lever negotiation playbook for Minneapolis data scientists
Lever 1: Use the BLS P75 as your base anchor, not the median. The $144,200 P75 figure is government data covering the actual distribution of Minneapolis data scientist salaries. If you have four or more years of relevant experience, a production ML portfolio, and have cleared a technical screen, anchoring your base ask in the $135K-$148K range for a senior role is defensible and not aggressive. Most Minneapolis hiring managers at large corporate employers know the BLS data; referencing it explicitly — “BLS OEWS puts the 75th percentile for this role in this metro at $144K, and I’m targeting that range given my background” — lands as market-informed rather than entitled. Anchoring at the P50 median signals you’ve done basic research but no more, and it gives the company an easy path to offer you exactly what you asked for.
Lever 2: Push for clarity on bonus structure before you negotiate base. At many Minneapolis employers — particularly healthcare and financial services — the bonus formula matters as much as the base. A company paying 12% of base at target versus one paying 8% represents a $4,800 difference on a $120K base that compounds over time. Before you receive a number, ask: Is the bonus discretionary or formula-based? What was the actual payout percentage for the last two years? Are there individual performance gates that can reduce payout even in a strong company year? Companies with formula-driven bonus structures that have consistently paid out at or above target are effectively paying a higher guaranteed cash comp than their base number alone suggests — and that’s worth factoring into your comparison math. Conversely, if the bonus is fully discretionary, treat it as a nice-to-have and negotiate the base as if the bonus doesn’t exist.
Lever 3: Create optionality by interviewing across industry sectors simultaneously. Minneapolis’s dual-pool market — healthcare/insurance on one side, retail/consumer/fintech on the other — is unusual among mid-size metros and creates a specific negotiation opportunity. A competing offer from Target or Best Buy creates legitimate cross-sector leverage at UnitedHealth or Ameriprise, and vice versa. Healthcare employers know the retail and fintech markets pay higher bases for technically equivalent roles; walking in with a $140K offer from a retail tech team is a concrete number a healthcare manager can take to HR and actually move a band. The cross-sector dynamic is more powerful than a competing offer from the same industry, because it forces the company to justify its value proposition rather than just argue that their band is $5K higher than a peer’s. Given that Minneapolis data science hiring across its major employers tends to cluster in Q1 and Q3, timing simultaneous interview processes is genuinely achievable with some planning.
Data caveats
A few limitations worth keeping in mind when using BLS OEWS data:
Base salary only. The survey captures cash wages paid during the May 2024 survey reference period. Bonuses, equity, profit-sharing, and employer contributions to benefits are excluded. For Minneapolis, where equity is a smaller fraction of total comp than in coastal tech markets, the BLS understatement is real but smaller — roughly 15-25% rather than the 30-50% commonly cited for SF or Seattle tech roles.
Approximately 12-18 month lag. The OEWS survey reflects wages paid in May 2024, released in late 2024. By mid-2026, the Minneapolis data science market has continued recovering from the 2023-2024 hiring slowdown that followed the Fed tightening cycle; active roles at top employers are likely paying 5-10% above these BLS figures.
SOC 15-2051 is a broad bucket. It includes machine learning engineers, data analysts who carry the scientist title, quantitative analysts at financial firms, and biostatisticians at medical device companies. Data engineers (SOC 15-1243) and statisticians (SOC 15-2041) have separate codes and separate pay distributions. If the role you’re benchmarking is primarily pipeline and infrastructure work, the 15-1243 data is more relevant; if it’s clinical or social science statistical modeling, 15-2041 applies.
Metro sample sizes run smaller than national data. The Minneapolis-St. Paul-Bloomington MSA figures are directionally reliable, but the BLS confidence intervals on metro-level data are wider than on national or state-level aggregates. At the P90 specifically, a handful of high-earning outliers can shift the reported figure meaningfully from survey cycle to survey cycle. Supplement BLS base data with Levels.fyi’s Minneapolis-St. Paul area figures (which show a median total compensation around $136,500-$138,000 across all levels in recent submissions) and with salary ranges that increasingly appear on Minnesota-origin job postings as employers comply with multi-state transparency laws. The triangulation of BLS percentiles, Levels total comp, and posted ranges gets you to within 10-15% of what any specific Minneapolis offer should look like before negotiations begin.