Hire Data Science developers in Denver

What the published figures say about hiring this skill into the Denver-Aurora-Centennial, CO market, what a seat here actually costs, and how to run the search so it closes.

The local market for this occupation

Data Science developers are counted by the Bureau of Labor Statistics under Data Scientists. In the Denver-Aurora-Centennial, CO area the Bureau puts employment in that occupation at 4,510, with a median annual wage of $112,520. The national median for the same occupation is $120,230, so Denver sits about 6% below the country as a whole.

Ranked against every US metropolitan area where this occupation is separately published, Denver is 61 of 282 by median wage. Of the 28 markets covered on this site it is 19. Those two numbers together are a better guide to what an offer needs to look like than any single national figure, because they say where the market sits rather than what it averages.

The middle half of the local market runs from $97,180 to $147,950. That band, not the midpoint, is the number to carry into a budget conversation about a data scientist here. Where in the band a particular hire lands depends far more on what the person can be left to own than on how many years the CV shows, which is why the level definitions matter more than the title.

One qualification applies to all of this and it is worth stating plainly. The Bureau classifies by occupation, not by technology. Nobody publishes an official wage figure for Data Science specifically, and any site that quotes one has either modelled it or made it up. The occupation figures are the honest available baseline: they describe the market a data scientist is hired into, and the technology adjusts where inside that band a given person sits.

What each level looks like against local pay

Percentiles describe a market; they do not describe a person. The useful step is to read the published Data Scientists band for Denver against what someone at each level can actually be left to own. The mapping below is a working guide rather than a rule, and the overlap between adjacent levels is genuine: a strong mid-level engineer can be worth more than a weak senior one and frequently is.

Bands are percentiles of the published local occupation wage, not FuturByte rates, and not a guarantee that any individual sits where the band suggests.

Two things go wrong when this mapping is used carelessly. The first is budgeting a senior seat at the local median and then interviewing people who can genuinely own an area of the system; in Denver that band starts around $112,520 and an offer below it will not close against a counter. The second is the reverse: paying at the top of the band for someone who still needs their work scoped by somebody else. The level definitions are there so the conversation is about what the person will own rather than about how many years the CV shows.

It is also worth being explicit that years and level are only loosely related in Data Science work. Somebody who has spent six years maintaining one application carries less transferable judgement than somebody who has spent three across three very different ones. When a CV and a band disagree, the interview should settle it, not the CV.

The titles you are bidding against locally

A data scientist in Denver is not only being recruited by other teams hiring the same title. The same person is a credible candidate for several adjacent occupations, and what those pay locally is part of what any offer has to clear. These are the published local figures for the titles that compete for this pool.

Where a row is missing, the Bureau does not publish a separate estimate for this metro, usually because the local sample is too small to release.

The spread across these titles in Denver runs from $203,340 for computer and information systems managers down to $92,060 for web developers. That gap is the practical reason technical people move sideways between titles rather than up within one: in this market the fastest available pay rise for a competent engineer is often a change of job title rather than a change of employer. If you are hiring at the lower end of that range, expect to lose some candidates to the upper end of it, and expect that to happen after they have accepted.

This also affects how a role should be written. A specification that describes the work in terms of one narrow title competes only for people who already hold it. One that describes the system and the ownership on offer reaches people currently sitting under a different title who would be entirely capable of the work, and that is usually where the available capacity in a tight market actually is.

Which direction this market is moving

A single year tells you the price. Three tell you whether it is going up. These are the published figures for data scientists in the Denver-Aurora-Centennial, CO area across the last three releases.

Source: BLS Occupational Employment and Wage Statistics, metropolitan area files. The Bureau does not design these releases to be read as a time series; treat the movement as a direction rather than as a growth rate.

Across those two years the local median moved up 1%. Employment moved up 4% over the same period. For someone planning a data scientist hire in Denver, the practical reading is that a salary band set from figures more than a year old is probably still close enough to work from.

Markets a candidate here would also consider

Candidates do not compare your offer against a national average. They compare it against what they could get nearby, and for a data scientist in Denver that means a handful of specific metros. These are the closest comparisons on the published figures.

Percentages compare each metro against Denver rather than against the national median.

None of the neighbouring markets pays materially more for this occupation, which is a genuine advantage when you are hiring here: local candidates are less likely to be pulled away by a nearby offer, and the competition you face is more likely to be fully remote employers than regional ones.

The same table read the other way is a sourcing map. If the local pool is thin and a neighbouring metro pays less for the same occupation, that metro is where a remote or relocating candidate is most likely to come from, and the conversation is easier because the move is upward for them.

Where Data Science work actually sits in this economy

The Denver economy is anchored by aerospace, cable and satellite, outdoor commerce and energy technology. Data Science is not used identically across those, and the version of the skill that is abundant locally is shaped by whichever of them employs the most engineers. That is the part a national salary table cannot tell you and it is usually what decides whether a shortlist converts.

Aerospace

Where Data Science appears in aerospace, it most often looks like the product analytics pattern: Understanding user behaviour, measuring feature impact and designing the experiments that inform the roadmap. Developers coming out of this part of the Denver market therefore tend to arrive strong on the constraints that sector imposes and lighter on the ones it never had to deal with. If your product shares those constraints, that is experience you would otherwise spend a year building. If it does not, the gap is real, and it is a fair thing to ask about directly rather than to discover in month two.

Cable and satellite

Where Data Science appears in cable and satellite, it most often looks like the operations pattern: Demand forecasting, capacity planning and supply chain analysis. Developers coming out of this part of the Denver market therefore tend to arrive strong on the constraints that sector imposes and lighter on the ones it never had to deal with. If your product shares those constraints, that is experience you would otherwise spend a year building. If it does not, the gap is real, and it is a fair thing to ask about directly rather than to discover in month two.

Outdoor commerce

Where Data Science appears in outdoor commerce, it most often looks like the marketing and growth pattern: Attribution, campaign measurement, segmentation and lifetime value modelling. Developers coming out of this part of the Denver market therefore tend to arrive strong on the constraints that sector imposes and lighter on the ones it never had to deal with. If your product shares those constraints, that is experience you would otherwise spend a year building. If it does not, the gap is real, and it is a fair thing to ask about directly rather than to discover in month two.

Energy technology

Where Data Science appears in energy technology, it most often looks like the pricing and revenue pattern: Elasticity, discount effectiveness and forecasting, where conclusions translate directly into money. Developers coming out of this part of the Denver market therefore tend to arrive strong on the constraints that sector imposes and lighter on the ones it never had to deal with. If your product shares those constraints, that is experience you would otherwise spend a year building. If it does not, the gap is real, and it is a fair thing to ask about directly rather than to discover in month two.

The practical use of this is in reading CVs rather than in sourcing. Two candidates in Denver with the same number of years of Data Science can have been solving quite different problems, and the interview should be aimed at the difference rather than at the technology they have in common.

What a local data scientist seat costs to keep open

Salary is the quoted number and it is not the budget. Below is the employer-side payroll cost of one person in this occupation in Denver, using published local wages and the statutory 2026 employer rates.

Employer OASDI at 6.2% to a wage base of $184,500, Medicare at 1.45% uncapped. Source: Social Security Administration, Contribution and Benefit Base, retrieved 2026-09-25. Unemployment insurance, benefits, equipment and recruitment cost are additional.

Then there is the cost nobody puts in the model. Every month the role is open, the work it was meant to do is not happening. That cost is the same whichever way you eventually fill the seat, and in a specialist search it routinely exceeds the difference between the options being compared. It is the single most common reason a cost comparison that looked careful turns out to have been wrong.

What to test when the pool is this one

The full interview guide for this technology is on the Data Science developers page. What changes in Denver is emphasis rather than substance: given what the local market has been building, these are the areas where candidates here differ most from each other, and therefore where an interview earns its keep.

An analysis that changed a decision

The outcome the role exists for.

• Strong answer: Describes the question, the work, the recommendation and what was actually done.
• Warning sign: Describes analyses produced but cannot name anything that changed as a result.

Data quality

Real data is messy and conclusions inherit its problems.

• Strong answer: Validates assumptions, checks for selection effects and survivorship bias, and has caught an error before publishing.
• Warning sign: Takes the data at face value.

When not to model

Tests judgement, which is what separates useful from impressive.

• Strong answer: Recognises when descriptive work answers the question and says so.
• Warning sign: Reaches for a model regardless of the question.

Two patterns worth asking about directly, because they show up in inherited codebases far more often than candidates volunteer them:

Ignoring how the data was collected

• What you see: Conclusions drawn without considering who is missing from the dataset.
• What it costs: Survivorship and selection effects producing confident conclusions about a population that was never observed.
• The fix: Ask what the data would look like if the hypothesis were false, and who is absent from it.

Dashboards nobody uses

• What you see: Extensive reporting infrastructure with no identified decision attached.
• What it costs: Maintenance burden and analyst time spent on output that changes nothing.
• The fix: Start from a decision somebody makes. Build the smallest thing that informs it.

The options, and what actually decides between them

Once the local figures are on the table, most teams are choosing between three ways of getting Data Science capacity into Denver. They are not ranked. Which one is right depends on how long the work lasts, how much of it there is, and how much of the surrounding context the person needs to hold.

Hiring locally onto your own payroll

The right answer when the work is permanent, when the person needs to accumulate context that has no value anywhere else, or when presence in Denver is a genuine requirement rather than a preference. The costs are the ones in the table above plus benefits and recruitment, and the risk is time: in a market this concentrated the search itself is usually quick and the closing is where offers are lost.

Adding a vetted developer to your existing team

Staff augmentation suits work that is real but not permanent, and teams that already have the review capacity and the architectural direction in place. The person joins your standups, your repository and your process. What you are buying is capacity and specific Data Science experience, not decision-making, and the constraint is almost always how much code your existing team can review rather than how many developers you add.

A dedicated team that owns an area

The right shape when there is a whole area of work to own rather than a queue of tickets, and when you would otherwise be hiring three or four people at once into a market where that takes a year. It asks more of you at the start, because an area cannot be owned without a clear definition of what it includes and who decides, and it asks less of you afterwards.

The comparison people get wrong is between a local salary and an hourly rate. Those are not the same quantity. A fair comparison puts the fully loaded employer cost of a local seat, including the months it stands empty and the recruitment spend that filled it, against the total cost of the alternative including the coordination overhead it adds. Run honestly, that comparison sometimes favours hiring locally, and when it does we will say so.

A realistic plan for this search

Compress the process before you start

With Data Science work this concentrated in Denver, the candidates worth hiring are in several conversations at once. Fix the panel, the decision-maker and the offer range before the first call. Processes that add a stage midway lose the people they were trying to be careful about.

Plan for the counter-offer

Expect the current employer to respond, and decide in advance whether you will match, improve or walk. Improvising that decision under a deadline is how teams end up overpaying for a hire who leaves in a year anyway.

Give the hire something to land on

A running environment on day one, a named reviewer and access to the real system rather than a mock. The difference between a first merged change in week one and in week four is almost entirely on your side of the table, not the candidate's.

Write the brief around the system, not the stack

State what the system does, what it runs on, what is already decided and what the new person would own. A list of technologies with no context filters for keyword matches, and keyword matches are exactly who gets rejected in the technical round.

What changes when the developer is not in the building

Most of what makes a distributed data scientist productive is decided in their first two weeks, and almost all of it is on the client side. These are the things that reliably separate a first merged change in week one from a first merged change in week four.

A running environment on day one

Not documentation describing how to build one. An environment that starts, with seed data, on the machine the developer actually has. Every day spent on environment setup is a day billed at full rate for no output, and it is the single most common avoidable cost in an engagement.

A named reviewer with real capacity

Someone whose job explicitly includes reviewing this work, not someone who will get to it. A developer who waits two days for review does a quarter of the work they otherwise would, and the cost of that lands on you.

Access to the real system, not a mock

Staging with representative data, the actual API, the actual error messages. Work built against a simplified mock has to be rebuilt when it meets production, and that rework is invisible until it is expensive.

A first task that is small and real

Something that ships in the first few days. It proves the environment, the review path and the deployment path all work, and it surfaces the broken one while it is still cheap to fix.

None of this is specific to working with us. It is what any developer joining any team needs, and it is worth stating plainly because the failures above get attributed to the developer far more often than to the setup that produced them.

The same role in other US markets

Ranked by how many people are employed in this occupation locally, which is the figure that most affects how long a search takes.

• Data Science developers in New York $135,980
• Data Science developers in San Francisco $170,110
• Data Science developers in Dallas-Fort Worth $127,750
• Data Science developers in Los Angeles $129,740
• Data Science developers in Washington, D.C. $132,200
• Data Science developers in Seattle $164,740
• Data Science developers in Chicago $107,640
• Data Science developers in Boston $132,040
• Data Science developers in Atlanta $108,940
• Data Science developers in Philadelphia $109,910
• Data Science developers in San Jose $185,080
• Data Science developers in Charlotte $132,460
• Data Science developers in Houston $106,750
• Data Science developers in Detroit $103,330
• Data Science developers in Austin $127,360

Other technologies in Denver

Skills that appear alongside Data Science on most job specifications.

• Machine Learning developers in Denver
• AI Engineering developers in Denver
• Python developers in Denver
• SQL developers in Denver
• Data Engineering developers in Denver

For the technology itself, including the full interview guide, migration paths and what each level can own, see hiring Data Science developers. For the wider Denver technical market across every occupation, see hiring developers in Denver.

Frequently asked questions

What does a data scientist cost in Denver?

There is no official wage figure for Data Science specifically, because the Bureau of Labor Statistics classifies by occupation rather than by technology. The honest baseline is Data Scientists in the Denver-Aurora-Centennial, CO area, where the median annual wage is $112,520 and the middle half of the market runs from $97,180 to $147,950. Those are employer wages, before payroll taxes, benefits and recruitment cost.

How many data scientists are there in Denver?

Nobody counts developers by technology, so any specific number you see quoted is an estimate. What is published is the occupation: 4,510 people in data scientists in the Denver-Aurora-Centennial, CO area. Data Science is one technology inside that population, and the share using it is a matter of inference rather than record.

Is it faster to hire a data scientist locally in Denver or remotely?

With the occupation this concentrated in Denver, local sourcing is usually quick and closing is the slow part, because good candidates have options and current employers counter. Remote widens the pool but does not remove the closing problem.

Do we need someone in the Denver time zone?

Usually less than teams assume. What genuinely needs the local clock is live incident response, work with people who are only available in local hours, and anything tied to a physical site. Everything else needs a committed overlap window of a few hours rather than a matching working day. Denver runs on Mountain time.

Which local industries will a data scientist here have come from?

The anchors of this economy are aerospace, cable and satellite, outdoor commerce and energy technology. Most experienced candidates in this market will have spent time in at least one of them, and that background shapes both what they are good at and what they have never had to handle. It is worth asking about explicitly rather than inferring from the CV.

Can you supply a data scientist who overlaps with Denver hours?

Yes. Overlap is the thing we schedule around rather than a side effect of where someone happens to live, and it is agreed before an engagement starts rather than negotiated afterwards. Tell us which hours genuinely need to be covered and why, and we will tell you whether we can meet it.

How do you assess a data scientist before we see them?

Working code and a conversation about decisions, not a quiz. We look at what someone has built, ask what they would now do differently and why, and probe the areas where this technology most reliably separates people. You see our reasoning alongside the shortlist, including the reservations.

What if the shortlist is wrong?

Tell us why and we will recalibrate. A rejected shortlist normally means the brief and the need had drifted apart, and that is worth finding out in week one rather than month three. We would rather say we are not the right fit for a role than keep sending candidates against a brief that is not working.