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Data Science Jobs in Colorado (NOW HIRING)

Cushman & Wakefield

Junior Data Scientist

Denver, CO

This position will report to the Head of Data Science and Geospatial Analytics. The ideal candidate is a practitioner who thinks first like an economist, real estate analyst, or quantitative urban ...

US Department of the Air Force

$49K/yr

Mathematics, statistics, computer science, data science or field directly related to the position. The degree must be in a major field of study (at least at the baccalaureate level) that is ...

Hercules Industries

Data Scientist

Denver, CO · On-site

Required : • Bachelor's degree in data science, Statistics, Mathematics, Engineering, Economics, or related field (Master's preferred) • 3-7+ years of experience in data science, advanced ...

Prescient Edge

Senior Data Scientist

Colorado Springs, CO · On-site

$135K - $210K/yr

Conduct data science functions on structured and unstructured data to streamline intelligence analysis and production, enrich results, and develop and maintain Python code. * Author cogent and ...

Radiance Technologies

Data Scientist

Colorado Springs, CO · On-site

$150K - $175K/yr

Bachelor's degree in Computer Science, Data Science, Engineering, Mathematics, or another relevant STEM discipline (or equivalent experience). * Five (5) or more years of experience as a Data ...

US Department of the Air Force

CO

$49K/yr

Mathematics, statistics, computer science, data science or field directly related to the position. The degree must be in a major field of study (at least at the baccalaureate level) that is ...

GRVTY

Data Scientist 3

Colorado Springs, CO · On-site

Make and communicate principled conclusions from data using elements of mathematics, statistics, computer science, and application specific knowledge. Through analytic modeling, statistical analysis ...

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$39.4K

$129.1K

$206.6K

How much do data science jobs pay per year?

As of Aug 6, 2026, the average yearly pay for data science in Colorado is $129,062.00, according to ZipRecruiter salary data. Most workers in this role earn between $103,600.00 and $143,000.00 per year, depending on experience, location, and employer.

Is a data scientist in high demand?

Yes, data scientists are in high demand across many industries due to the increasing reliance on data-driven decision making. The role requires skills in programming, statistics, and machine learning, and job growth is expected to continue as organizations expand their data capabilities.

What are the key skills and qualifications needed to thrive as a data scientist, and why are they important?

To thrive as a Data Scientist, you need a strong background in statistics, programming (often Python or R), and data analysis, usually supported by a degree in a quantitative field. Familiarity with machine learning libraries (like scikit-learn or TensorFlow), big data tools (such as Hadoop or Spark), and data visualization platforms is typically required. Critical thinking, problem-solving, and effective communication are vital soft skills for translating complex data insights into actionable business strategies. These skills and qualities are essential for extracting value from data, driving informed decisions, and effectively collaborating with multidisciplinary teams.

What are some common challenges faced by data scientists when working with real-world datasets?

Data scientists often encounter challenges such as missing or inconsistent data, unstructured formats, and noisy information in real-world datasets. Cleaning and preprocessing data to ensure its quality can be time-consuming but is critical for building accurate models. Additionally, data scientists may work closely with domain experts and other team members to better understand the data's context and ensure their analyses align with business objectives. Overcoming these challenges requires strong problem-solving skills and effective collaboration within cross-functional teams.

What is data science?

Data science is an interdisciplinary field that uses scientific methods, algorithms, and systems to extract insights and knowledge from structured and unstructured data. It combines skills from statistics, computer science, and domain expertise to analyze and interpret complex data sets. Data scientists work with large amounts of data to identify patterns, make predictions, and help organizations make data-driven decisions.

What is the difference between Data Science vs Data Analyst?

AspectData ScienceData Analyst
Required skillsStatistics, programming (Python, R), machine learningData visualization, SQL, basic statistics
Work environmentDeveloping models, predictive analytics, researchReporting, data cleaning, descriptive analysis
Tools usedPython, R, Jupyter, TensorFlowExcel, SQL, Tableau, Power BI
Industry usageTech, finance, healthcare, e-commerceRetail, marketing, finance, healthcare

Data Science and Data Analyst roles often overlap but differ mainly in scope. Data Scientists focus on building predictive models and advanced analytics, requiring programming and machine learning skills. Data Analysts primarily handle data cleaning, reporting, and visualization. Both roles are essential in data-driven industries, but Data Science is more technical and research-oriented, while Data Analysis emphasizes interpreting data for business insights.

What does a data scientist do?

As a Data Scientist, you are qualified to work in such diverse fields as research and development, politics, advertising and marketing, technology, healthcare, government, and higher education as well as multiple others. In general, your duties and responsibilities will be to compile and analyze relevant statistics and turn those numbers into algorithms that reveal insights that can be used by other researchers in their areas of study. Data Science can reveal things like consumer buying habits or the likelihood of success for a course of action. Other duties might vary, depending on your unique field of specialty. Related areas in which a Data Scientist might wish to focus include work as a Data Analyst, Machine Learning Engineer, and Project Manager.
More about Data Science jobs
What are the most commonly searched types of Data Science jobs in Colorado? The most popular types of Data Science jobs in Colorado are:
What are popular job titles related to Data Science jobs in Colorado? For Data Science jobs in Colorado, the most frequently searched job titles are:
What job categories do people searching Data Science jobs in Colorado look for? The top searched job categories for Data Science jobs in Colorado are:
What cities in Colorado are hiring for Data Science jobs? Cities in Colorado with the most Data Science job openings:
Data Science jobs near you
Infographic showing various Data Science job openings in Colorado as of July 2026, with employment types broken down into 1% As Needed, 82% Full Time, 12% Part Time, 1% Temporary, and 4% Contract. Highlights an 88% Physical, 3% Hybrid, and 9% Remote job distribution, with an average salary of $129,062 per year, or $62 per hour.

Junior Data Scientist

Cushman & Wakefield

Denver, CO

Full-time

Medical, Dental, Vision, Life, Retirement

Re-posted 12 days ago

Cushman & Wakefield rating

7.4

Company rating: 7.4 out of 10

Based on 158 frontline employees who took The Breakroom Quiz

110th of 201 rated real estate companies

Job description

Job Title

Junior Data Scientist

Job Description Summary

This role sits at the intersection of real estate economics, urban analysis, and data science. The Junior Data Scientist will support the development and evolution of Cushman and Wakefield Quantitative Insight Group's (QIG) analytical capabilities by producing rigorous, insight-driven work on commercial real estate markets across the Americas. This position will report to the Head of Data Science and Geospatial Analytics. The ideal candidate is a practitioner who thinks first like an economist, real estate analyst, or quantitative urban planner, and who brings the technical skills to build and operate the data infrastructure their own work requires.
This is not primarily an engineering role, though the ideal candidate will possess data engineering knowledge, skills, and abilities. The Analyst will spend most of their time doing substantive analytical and research work: synthesizing complex datasets, identifying market patterns and anomalies, and producing outputs that inform Cushman & Wakefield's House View, including elements that are unique to QIG, and related analytical products for key clients. At the same time, the candidate should be comfortable constructing and maintaining data pipelines, working fluently in Python and/or R and SQL, and collaborating closely with Technology & Data Solutions (TDS) as a knowledgeable and credible partner.

Job Description

Key Responsibilities

Real Estate & Urban Economic Analysis (45%)

  • Conduct rigorous quantitative analysis on commercial real estate markets, synthesizing property, macroeconomic, and urban data to surface market trends, structural shifts, and investment-relevant insights.

  • Apply econometric and statistical methods (time series modeling, regression, spatial econometrics, or similar) to real estate and labor market questions in support of QIG research products.

  • Integrate geospatial data and methods into analytical workflows: working with Census geographies, parcel data, land use classifications, walkability or transit metrics, demographic overlays, and similar inputs to enrich market analysis.

  • Contribute to the development of novel datasets and indicators that advance QIG's analytical edge, including working closely with the Head of Data Science & Geospatial Analytics to specify and build integrated data products combining proprietary CRE data with public and third-party sources.

  • Support the QIG team on ad hoc analytical requests from Americas Research, the Global Think Tank, and senior stakeholders, producing clean, well-documented, and reproducible outputs.

Data Engineering & Pipeline Maintenance (35%)

  • Build andmaintainautomated data pipelines for ingesting, transforming, and storing CRE and macroeconomic datasets used in analytical modelsand reoccurring analysis.

  • Ensure data integrity and consistency across QIG inputs and outputs through validation, quality control procedures, and structured data interfaces.

  • Perform exploratory data analysis and profiling on raw and processed datasets tovalidatepipeline outputs andidentifyanomalies or inconsistencies.

  • Partner with PRI (Property Research & Intelligence), TDS (Technology & Data Solutions), and the GIS team to ensure governance of time series and geospatial data, particularly as geography-based competitive sets evolve.

  • Serve as a knowledgeable liaison to TDS: translating analytical requirements into engineering specifications, tracking the status of data requests in the TDS backlog, and validating outputs against analytical expectations.

Documentation, Integration & Infrastructure (20%)

  • Develop andmaintaininternal documentation covering data sources, model architecture, data flows, and diagnostic procedures, with attention to field-level lineage and traceability.

  • Serve as the team's subject matter expert on integration and processing of internal, third-party vendor, and public datasets (e.g., Census TIGER, IPUMS, LODES, NLCD, Overture Maps), and advise on cleaning, normalization, andappropriate analyticalapplications.

  • Monitor the evolution of third-party data products; assess their fit against QIG analytical requirements and produce intake specifications when new sources are approved for integration.

  • Support the adoption of emerging analytical technologies (including ML/AI methods and advanced data infrastructure patterns) through hands-on prototyping and coordination with TDS whereappropriate.

Qualifications

  • Bachelor's degree inEconomics,Data Science,Real Estate, Applied Economics, Geography,UrbanPlanningor anyclosely related field with quantitative emphasis.A master's degree ispreferredand adoctoral degree is a plus.

  • 2 to 6yearsof experience ina research, analytical, or data science role, preferably in a real estate, urban policy, planning, or economic research context.

  • Strong command of quantitative methods: regression,time series analysis,spatial econometrics, or comparable approaches applied to real estate or urban economic questions.

  • Working knowledge of geospatial data and methods: experience with GIS tools (ArcGIS, QGIS, or programmatic approaches via R or Python), familiarity with spatial data formats and concepts, and comfort integrating geographic context into analysis.

  • Proficiencyin Python and/or R for data analysis, modeling, and pipeline construction; working knowledge of SQL. Familiarity with cloud platforms (Azure, AWS) and version control is a plus.

  • Experience working with public datasets commonly used in urban and real estate research: Census products (ACS, TIGER, LODES), BLS, IPUMS, or similar.

  • Ability to produce clean, well-documented, reproducible analytical work and communicate findings clearly to both technical and non-technical audiences.

  • Comfortable operating in a cross-functional environment, working both independently and alongside engineering and research teams on iterative deliverables.

  • Genuine intellectual interest in urban economics, commercial real estate markets, and the spatial dimensions of economic activity.

  • Comfortability in communicating analysis, methods and related topics withrelated teams and immediate management.


Cushman & Wakefield also provides eligible employees with an opportunity to enroll in a variety of benefit programs, generally including health, vision, and dental insurance, flexible spending accounts, health savings accounts, retirement savings plans, life, and disability insurance programs, and paid and unpaid time away from work. In addition to a comprehensive benefits package, Cushman and Wakefield provide eligible employees with competitive pay, which may vary depending on eligibility factors such as geographic location, date of hire, total hours worked, job type, business line, and applicability of collective bargaining agreements.
The compensation that will be offered to the successful candidate will depend on factors such as whether the position is covered by a collective bargaining agreement, the geographic area in which the work will be performed, market pay rates in that area, and the candidate's experience and qualifications.
The company will not pay less than minimum wage for this role.
The compensation for the position is: $ 114,750.00 - $135,000.00Cushman & Wakefield is an Equal Opportunity employer to all protected groups, including protected veterans and individuals with disabilities. Discrimination of any type will not be tolerated.

In compliance with the Americans with Disabilities Act Amendments Act (ADAAA), if you have a disability and would like to request an accommodation in order to apply for a position at Cushman & Wakefield, please call the ADA line at 1-888-365-5406 or emailAccommodations@cushwake.com. Please refer to the job title and job location when you contact us.

INCO: "Cushman & Wakefield"

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