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About This Role
Data Scientist
Job Description
AI\-Data Scientist \- CoStar Group\-Richmond , VA
Company Overview:
CoStar Group (NASDAQ: CSGP) is a leading global provider of commercial and residential real estate information, analytics, and online marketplaces. Included in the S\&P 500 Index, CoStar Group is on a mission to digitize the world’s real estate, empowering all people to discover properties, insights and connections that improve their businesses and lives.
We have been living and breathing the world of real estate information and online marketplaces for over 35 years, giving us the perspective to create truly unique and valuable offerings for our customers. We’ve continually refined, transformed, and perfected our approach to our business, creating a language that has become standard in our industry, for our customers, and even our competitors. We continue that effort today and are always working to improve and drive innovation. This is how we deliver for our customers, our employees, and investors. By equipping the brightest minds with the best resources available, we provide an invaluable edge in real estate.
About Brand:
CoStar is the global leader in commercial real estate information, analytics , and news. Commercial Real Estate industry professionals around the globe use CoStar to access the most comprehensive data to make decisions with confidence. CoStar delivers immediate, verified commercial real estate information on over 5\.9 million properties across every market.
Learn more about CoStar .
Role Description:
We are seeking a Data Scientist to build enhancements to our data quality and research processes. The successful candidate will have a strong background in machine learning, artif icial intell igence , and statistical modeling, with a proven ability to develop new models, optimize existing processes, and deliver business impact through data\-driven solutions. Familiarity with commercial real estate concepts and datasets is preferred, though a demonstrated ability to learn complex domains quickly is valued. This individual will play a key role, working closely with product, engineering, and research leadership teams to improve our data quality and completeness.
This is a full\-time in\-office position that will be based in our Richmond, VA office.
R esponsibilities :
- Develop and evaluate machine learning models that support CoStar Research’s goals and enhance data quality
- Lev erage fro ntier arti fic ial intelligence models , pot entially incl uding co mputer visio n and mult i\- moda l approach es, to im prove the quality and timelin ess of k ey busine ss proces s es and wor kflows
- Conduct rigorous experimentation and model validation to ensure accuracy, robustness, and fairness
- Collaborate with research leaders, product teams, and engineers to understand requirements and translate them into data\-driven solutions
- D e monstr ate output in the form of depl oyed demos o r profession al quality visua lization s for both t echnical an d non \-technical audienc es
- Monitor model performance and proactively retrain or refine as necessary
B asic Qualifications:
- Bachelor’s degree in computer science, data analytics, statistics, machine learning, or related field
- 2\+ years of relevant experience in machine learning, arti ficial intelligen ce, model performance, statistical modeling, and data preparation
- Understanding of machine learning and statistical modeling techniques, including supervised and unsupervised methods
- Familiarity with model evaluation metrics, validation strategies, and performance tuning techniques
- Proficiency in Python and key ML/data libraries such as P yTorch
- Experience working with large and complex datasets
- Comfortable collaborating in cross\-functional teams and communicating technical concepts clearly to non\-technical audiences
P referred Qualifications :
- Experience incorporating one or more Large Language Model into a business process or application
- Working experience with AWS or other major cloud providers
- Familiarity with commercial real estate data or concepts
- Experience working with comput er vision or m u lt i \- modal models
W hat's in it for you?
When you join CoStar Group, you’ll experience a collaborative and innovative culture working alongside the best and brightest to empower our people and customers to succeed.
We offer you generous compensation and performance\-based incentives. CoStar Group also invests in your professional and academic growth with internal training, tuition reimbursement, and an inter\-office exchange program.
Our benefits package includes (but is not limited to):
- Comprehensive healthcare coverage: Medical / Vision / Dental / Prescription Drug
- Life, legal, and supplementary insurance
- Virtual and in person mental health counseling services for individuals and family
- Commuter and parking benefits
- 401(K) retirement plan with matching contributions
- Employee stock purchase plan
- Paid time off
- Tuition reimbursement
- On\-site fitness center and/or reimbursed fitness center membership costs (location dependent), with yoga studio, Pelotons, personal training, group exercise classes
- Access to CoStar Group’s Employee Resource Groups
- Complimentary gourmet coffee, tea, hot chocolate, fresh fruit, and other healthy snacks
The final salary or hourly rate offered for this role will fall within the range set forth below based on a variety of factors, including but not limited to, geographic location, skills, and competencies.
Base Compensation: $71,400 to $105,000 annual base.
We welcome all qualified candidates who are currently eligible to work full\-time in the United States to apply. However, please note that CoStar Group is not able to provide visa sponsorship for this position.
\#LI\-CH1
CoStar Group is an Equal Employment Opportunity Employer; we maintain a drug\-free workplace and perform pre\-employment substance abuse testing
Salary Context
This $71K-$105K range is in the lower quartile for Data Scientist roles in our dataset (median: $160K across 258 roles with salary data).
View full Data Scientist salary data →Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 4,317 AI roles we're tracking, Data Scientist positions make up 8% of the market. At CoStar Group, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $192,890 based on 789 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($88K) sits 54% below the category median. Disclosed range: $71K to $105K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
CoStar Group AI Hiring
CoStar Group has 1 open AI role right now. They're hiring across Data Scientist. Based in Richmond, VA, US. Compensation range: $105K - $105K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 median).
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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