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About This Role
At Compass, our mission is to help everyone find their place in the world. Founded in 2012, we're revolutionizing the real estate industry with our end\-to\-end platform that empowers residential real estate agents to deliver exceptional service to seller and buyer clients.
About the Role:
We are seeking a highly skilled and motivated Lead Data Scientist to join our data science team. In this role, you will leverage your deep expertise in machine learning, statistical modeling, and data analysis to solve our most complex problems. You will set the standard for data science excellence, architect scalable solutions, and help define the long\-term analytical strategy. You will partner with senior business stakeholders and engineering leaders to uncover insights, develop cutting\-edge data\-driven solutions, and drive initiatives that directly shape company\-wide strategic planning, resource allocation, and product innovation.
Responsibilities:
- Lead the end\-to\-end development, validation, and deployment of large\-scale predictive models and algorithms that inform strategic business decisions and market trend analysis.
- Design and execute rigorous data\-driven research to analyze the impact of multi\-faceted factors on business outcomes, tackling the organization's most highly ambiguous and open\-ended problems.
- Collaborate deeply with senior business stakeholders and engineering partners to identify strategic opportunities, translate overarching business goals into complex analytical frameworks, and deliver high\-impact actionable insights.
- Synthesize and communicate highly complex methodologies, technical trade\-offs, and strategic findings clearly to both C\-level executives and technical audiences.
- Act as a technical mentor to other data scientists, fostering a culture of continuous learning, rigorous peer review, and adherence to state\-of\-the\-art methodologies.
Qualifications:
- Master's degree or PhD in Computer Science, Statistics, Economics, Mathematics, or a related quantitative field.
- 5\+ years of experience in data science with a proven track record of conceptualizing, leading, and delivering highly successful, end\-to\-end data science projects.
- Deep expertise in key data science domains (e.g., time\-series forecasting, deep learning, causal inference).
- Extensive practical experience architecting solutions using a broad range of methodologies (e.g., prediction, segmentation, NLP).
- Demonstrated proficiency in Python and SQL for complex data manipulation, statistical analysis, and model development.
- Hands\-on experience productionizing machine learning models and strong familiarity with MLOps concepts and workflows.
- Proven experience leading complex, cross\-functional projects and applying advanced methodologies to solve ambiguous business problems.
- Strong advocate for clean code principles, software engineering best practices, and technical standards.
- Strong business acumen and strategic thinking, with a proven ability to understand the broader business context, evaluate tradeoffs, and align analytical projects with organizational goals.
- Experience mentoring and guiding junior team members, overseeing project quality, and investigating root causes of complex technical challenges.
- Exceptional communication and collaboration skills, with a proven ability to work effectively in a fast\-paced, cross\-functional environment.
- Experience in the Real Estate industry or other market\-driven domains is a plus.
Compensation: The base pay range for this position is $175,500\-195,000 annually; however, base pay offered may vary depending on job\-related knowledge, skills, and experience. Bonuses and restricted stock units may be provided as part of the compensation package, in addition to a full range of benefits. Base pay is based on market location. Minimum wage for the position will always be met.
Perks that You Need to Know About:
Participation in our incentive programs (which may include eligible cash, equity, or commissions). Plus paid vacation, holidays, sick time, parental leave, and recharge leave; medical, tele\-health, dental and vision benefits; 401(k) plan; flexible spending accounts (FSAs); commuter program; life and disability insurance; Maven (a support system for new parents); Carrot (fertility benefits); UrbanSitter (caregiver referral network); Employee Assistance Program; and pet insurance.
Do your best work, be your authentic self.
At Compass, we believe that everyone deserves to find their place in the world — a place where they feel like they belong, where they can be their authentic selves, where they can thrive. Our collaborative, energetic culture is grounded in our Compass Entrepreneurship Principles and our commitment to diversity, equity, inclusion, growth and mobility. As an equal opportunity employer, we offer competitive compensation packages, robust benefits and professional growth opportunities aimed at helping to improve our employees' lives and careers.
Notice for California Applicants
Los Angeles County Fair Chance Notice
Salary Context
This $175K-$195K range is above the median 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 Compass 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. Senior-level AI roles across all categories have a median of $227,400. Disclosed range: $175K to $195K.
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.
Compass Group AI Hiring
Compass Group has 2 open AI roles right now. They're hiring across Data Scientist. Based in New York, NY, US. Compensation range: $195K - $226K.
Location Context
AI roles in New York pay a median of $220,000 across 1,650 tracked positions.
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.
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