Data Scientist Salary in Pennsylvania: Pay by Experience, City & Take-Home

A data scientist in Pennsylvania earns a median of $106,850 a year, or about $51.37 an hour, according to the U.S. Bureau of Labor Statistics (2025). Most people in the role land between $63,340 at the 10th percentile and $162,290 at the 90th. That median sits roughly $13,000 below the national figure, which tells you most of what you need to know about pay in this state before you read another word.

How Pennsylvania compares to the national average

The national median for data scientists is $120,230, per BLS OEWS 2025. Pennsylvania’s median of $106,850 trails that by $13,380, or about 11 percent. That’s a real gap, and it’s worth understanding before you accept or counter an offer here.

Part of the difference is concentration. The highest data-science pay in the country clusters in the Bay Area, Seattle, and New York, where tech-company demand pulls the national median up. Pennsylvania has a strong base of employers in finance, healthcare, insurance, and pharma, but fewer of the FAANG-tier salaries that skew the top end. The state’s mean salary of $110,810 runs above the median, which is normal: a handful of senior and specialist roles at the top pull the average up while the typical worker sits at the median.

The flip side is cost. Pennsylvania’s flat 3.07 percent state income tax is one of the lowest in the country, and housing across most of the state costs far less than in the coastal metros that post bigger headline numbers. A $106,850 salary in Harrisburg or Pittsburgh stretches further than a $130,000 salary in San Francisco. The take-home and housing section below puts exact dollars on that.

It also helps to frame the gap in dollars rather than percent. The $13,380 difference between Pennsylvania’s median and the national median is real money, roughly $1,100 a month before tax. But the national median is itself inflated by a small number of very high-cost, very high-pay metros. Compared against most of the country, Pennsylvania’s $106,850 is a strong, livable data-science salary, and the state’s top earners at $162,290 are well into six figures by any standard. The question isn’t whether Pennsylvania pays well in absolute terms, it does; it’s whether a specific competing offer elsewhere clears the gap after you account for that location’s taxes and rent.

Data scientist salary in Pennsylvania by experience

Experience is the single biggest lever on pay in this field. BLS reports the full percentile spread for Pennsylvania, and it maps cleanly onto career stage. Entry-level roles cluster near the 10th and 25th percentiles; the median is your mid-career anchor; the 75th and 90th are where senior and lead data scientists live.

The jump from entry to median is the steepest part of the curve. A new data scientist starting near $63,340 can roughly double their pay over a career, reaching $129,160 at the 75th percentile and $162,290 at the top. That climb usually comes from three things: moving from analyst-adjacent work into production machine learning, picking up cloud and MLOps skills, and switching employers at least once. The gap between the 25th percentile ($79,220) and the 75th ($129,160) is nearly $50,000, which means two people with the same job title in the same state can be paid very differently based on stack, industry, and negotiation.

Put another way, the move from the 10th percentile to the median is worth $43,510 in gross pay, and the move from the median to the 90th is worth a further $55,440. The biggest single dollar jumps come in the middle of a career, not at the start and not at the very top. Early on, you’re proving you can ship reliable analysis; in the mid-band, you’re being paid for owning models that drive real business decisions. The reason the curve is so wide is that “data scientist” covers everything from a SQL-and-dashboards analyst on the low end to a machine-learning engineer building recommendation systems on the high end. Pennsylvania’s mix of insurance, pharma, and banking employers tends to pay solidly in the middle of that range, with the highest bands reserved for people who combine statistical depth with engineering and a clear business impact.

One practical note on reading these percentiles: they describe the existing workforce, not a guaranteed path. Reaching the 90th percentile of $162,290 in Pennsylvania almost always means a senior, staff, or principal title, often with team leadership or deep specialization. If your current offer sits near $79,220, that’s normal for an early-career role here, and the table tells you the realistic ceiling is more than double that if you keep building.

Data scientist salary by Pennsylvania metro

Where you work inside Pennsylvania matters as much as your experience. BLS OEWS 2025 breaks out median pay for the state’s largest metro areas, and the spread between the top and bottom metro is over $25,000.

Philadelphia is both the highest-paying and the deepest market, with 6,480 data scientists employed and a median of $109,910, above the statewide figure. That’s driven by the region’s pharma, insurance, and financial-services base, plus spillover from nearby New Jersey and Delaware employers. Pittsburgh is the second-largest market at 2,270 jobs, anchored by Carnegie Mellon’s talent pipeline and a growing robotics and AI cluster, but its median of $96,670 runs about $13,000 below Philadelphia. Allentown surprises on the high side at $101,970, helped by its proximity to the New York and New Jersey corridor. Scranton sits at the bottom at $84,750, with the smallest employment base at 200 jobs.

The employment numbers tell their own story. Philadelphia’s 6,480 data-science jobs make up nearly half of the state’s 13,810 total, so it isn’t just the best-paid market, it’s where most of the openings are. Pittsburgh adds another 2,270, which means the two big metros together account for the large majority of data-science employment in Pennsylvania. The smaller markets, Harrisburg (500), Allentown (490), and Scranton (200), have far fewer roles, so even when their medians look competitive, the volume of jobs is thin. If you’re early in your career and want options, the two large metros give you the most room to switch employers without relocating, which matters because job-hopping is the fastest way to move up the percentile table above.

It’s also worth noting how the metro medians sit against the statewide $106,850. Only Philadelphia clears it. Pittsburgh, Harrisburg, Allentown, and Scranton all fall below the state median, which means the statewide figure is pulled up largely by the Philadelphia region. If you benchmark an offer in Pittsburgh against the statewide number, you’ll think you’re being lowballed when you’re actually right at the local market rate of $96,670. Always compare against your metro, not the state.

The practical read: if you’re optimizing for raw salary, Philadelphia leads. If you’re optimizing for salary against cost of living, Pittsburgh, Harrisburg, and Allentown can leave you better off because housing is cheaper while the pay gap is smaller than the rent gap. Pittsburgh in particular pairs a $96,670 median with some of the most affordable housing of any major U.S. tech hub, which is why its lower headline number often beats Philadelphia’s on what you actually keep.

Take-home pay after taxes in Pennsylvania

Gross salary is the headline; take-home is the number that hits your account. Pennsylvania’s flat 3.07 percent income tax keeps state withholding low and predictable, but federal tax and FICA still take the biggest bites. The table below shows estimated take-home at each career stage for a single filer in tax year 2026.

At the median, a Pennsylvania data scientist keeps about $80,275 of a $106,850 salary, an effective rate of roughly 24.9 percent. That works out to $6,690 a month. Notice how small the state-tax line is relative to federal and FICA: just $3,280 at the median, thanks to the flat 3.07 percent rate.

The effective rate climbs steadily with income because federal tax is progressive while Pennsylvania’s is flat. An entry-level earner at $63,340 loses about 19.5 percent to taxes and keeps $50,988, or $4,249 a month. The experienced earner at $129,160 sits at roughly 26.4 percent and nets $95,069, about $7,922 monthly. At the senior level of $162,290, the effective rate reaches 28.1 percent and take-home is $116,696, or $9,725 a month. So while gross pay between entry and senior nearly triples, take-home grows a bit slower because higher federal brackets take a larger slice. FICA is the one near-constant: 7.65 percent on wages up to the Social Security cap, which is why its dollar amount scales almost linearly with pay until the cap.

The standout feature of these numbers is how flat the Pennsylvania state line stays. From entry to senior, state tax only moves from $1,945 to $4,982, always close to 3.07 percent of gross. In states with progressive income taxes, that state line can balloon at higher incomes; in Pennsylvania it doesn’t. That predictability is a genuine perk for high earners, and it’s the main reason the no-income-tax-state advantage is so small here.

Here’s the cross-state comparison that actually changes decisions. Take the same $106,850 salary to Texas, which has no state income tax, and the take-home rises to about $83,555. That’s a gap of $3,280 a year, the entire Pennsylvania state-tax bill. So a Texas data scientist on identical gross pay keeps $3,280 more annually. It’s a real number, but it’s modest: Pennsylvania’s flat tax is low enough that the no-tax-state advantage is far smaller here than it would be moving out of California or New York.

The number that truly matters is net pay minus local housing. A $3,280 tax saving means little if rent is $700 a month higher. Pennsylvania’s housing costs, especially outside Philadelphia, are well below the national big-metro average, so the state’s modest tax often nets out ahead of higher-pay, higher-cost states once you subtract what shelter actually costs you. Run your own number through the Pennsylvania paycheck calculator before you compare offers, and when tax season comes, software like TurboTax can handle the multi-state and remote-work wrinkles that trip people up.

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How to earn more as a data scientist in Pennsylvania

The percentile spread above shows the ceiling is high. Closing the distance between the median and the 75th or 90th percentile is mostly about skills, industry, and metro. Concrete moves that work here:

• Target Philadelphia employers. The metro’s $109,910 median beats Pittsburgh’s $96,670 by about $13,000. If you can work in or remote into the Philadelphia pharma, insurance, and financial-services market, that’s the highest base in the state.
• Move into production ML and MLOps. Data scientists who own models in production, not just notebooks and dashboards, sit at the top of the pay band. Cloud platforms (AWS, Azure, GCP) plus deployment and pipeline skills are what separate a $79,220 early-career role from a $129,160 experienced one.
• Get a recognized certification. AWS Certified Machine Learning, Google Professional Data Engineer, and Azure Data Scientist Associate are the credentials Pennsylvania employers actually screen for. You can build toward them with structured courses on Coursera and put a verifiable credential on your resume.
• Specialize in a regulated industry. Pennsylvania’s biggest data-science buyers are healthcare, pharma, insurance, and banking. Domain knowledge in clinical data, actuarial modeling, or fraud detection commands a premium because the talent pool is thinner.
• Switch employers to reset your base. The single fastest way to jump percentiles is changing jobs. Internal raises rarely match the gap between the 25th and 75th percentile; a move often does.
• Push toward lead and staff titles. The 90th percentile of $162,290 is largely lead, staff, and principal data scientists. Mentoring, owning roadmaps, and driving technical strategy is what gets you there.
• Negotiate against the right benchmark. Know your metro median before you talk numbers. If you’re in Pittsburgh, anchor to $96,670; in Philadelphia, anchor to $109,910. Walking into a negotiation with the local BLS figure and a clear case for the 75th percentile of $129,160 is far more persuasive than a vague “market rate.”

The fastest route through the percentile table is usually a combination, not a single move: a recognized cloud or ML certification, one or two production-grade projects you can point to, and a willingness to change employers. People who do all three tend to cross from the early-career band near $79,220 into the experienced band near $129,160 inside a few years, and that $50,000 swing is the most achievable big raise available in this field in Pennsylvania.

Job outlook for data scientists in Pennsylvania

Pennsylvania employs about 13,810 data scientists as of BLS OEWS 2025, with the bulk concentrated in Philadelphia (6,480) and Pittsburgh (2,270). Data science remains one of the faster-growing occupations nationally, and the state’s deep base in healthcare, pharma, insurance, and finance keeps demand steady even when consumer tech hiring cools. Pittsburgh’s AI and robotics cluster, fed by Carnegie Mellon, adds a research-heavy demand center that few states can match. When you’re ready to test the market, job boards like ZipRecruiter list active Pennsylvania data-science openings so you can benchmark live offers against the BLS figures above.

Related and higher-paying roles in Pennsylvania

Data science doesn’t exist in isolation, and adjacent roles in Pennsylvania pay differently. Using BLS OEWS 2025 medians for this state: a Software Developer earns about $126,550, which is $19,700 MORE than a data scientist in Pennsylvania. A Computer Systems Analyst earns about $98,550, which is $8,300 LESS than a data scientist here. If you’re weighing a pivot, the software-developer path carries the higher base in this state, while systems-analyst work trades some pay for a different day-to-day.

Frequently asked questions

Sources: U.S. Bureau of Labor Statistics, OEWS 2025 (SOC 15-2051); take-home estimates use 2026 federal, FICA, and Pennsylvania state tax rates for a single filer. Last updated 2026. See our methodology.