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About This Role
As a global leader in cybersecurity, CrowdStrike protects the people, processes and technologies that drive modern organizations. Since 2011, our mission hasn’t changed — we’re here to stop breaches, and we’ve redefined modern security with the world’s most advanced AI\-native platform. Our customers span all industries, and they count on CrowdStrike to keep their businesses running, their communities safe and their lives moving forward. We’re also a mission\-driven company. We cultivate a culture that gives every CrowdStriker both the flexibility and autonomy to own their careers. We’re always looking to add talented CrowdStrikers to the team who have limitless passion, a relentless focus on innovation and a fanatical commitment to our customers, our community and each other. Ready to join a mission that matters? The future of cybersecurity starts with you.
About the Role:
We are seeking a Lead Enterprise Data Scientist that will play a key role in developing and deploying AI\-driven insights and predictive analytics across our enterprise business applications. This role will focus on creating, maintaining, and delivering high\-impact data science solutions critical to business functions, including Sales, Marketing, Customer Success, and Go\-to\-Market (GTM) Operations. The successful candidate will also mentor other data scientists and collaborate closely with leaders throughout the organization.
What You'll Do:
- Lead the development of predictive models, advanced analytics, and automation solutions that improve forecasting, retention, financial planning, and operational performance.
- Help mentor and develop other data scientists, fostering collaboration, innovation, and delivery excellence.
- Partner with business leaders across the enterprise to define priorities, shape data\-driven strategies, and align analytics initiatives with organizational objectives.
- Work with enterprise data from Snowflake and other Data Lake solutions to ensure data is effectively extracted, transformed, and applied to key business initiatives.
- Drive the creation of impactful dashboards and visualizations in Tableau, ensuring insights are accessible and actionable for executives and functional leaders.
- Present findings, trends, and predictive model outcomes to senior stakeholders, translating complex analytics into clear recommendations.
- Champion data governance, quality, and compliance standards in analytics and reporting.
- Stay current with trends in AI, ML, and business analytics, identifying opportunities for innovation and automation across the organization.
What You'll Need:
- 7\+ years of experience in data science, predictive analytics, and AI.
- Proven track record of balancing technical excellence with business impact.
- Expertise in Python, R, or SQL for data manipulation, statistical modeling, and machine learning.
- Hands\-on experience with Snowflake or other cloud\-based Data Lake solutions for enterprise data processing.
- Strong proficiency in Tableau (preferred BI tool) for data visualization and reporting.
- Demonstrated ability to communicate complex findings to executive leadership and business stakeholders in a clear, actionable manner.
- Experience partnering with multiple business units (Sales, Finance, Customer Success, Operations) to drive analytics adoption and strategy.
- Strong understanding of business KPIs, forecasting methods, customer segmentation, and operational analytics.
- Ability to oversee large\-scale enterprise data initiatives while ensuring scalability, efficiency, and security.
- Excellent storytelling and communication skills, with the ability to align data science strategy to enterprise objectives.
Bonus Points:
- Familiarity with cloud platforms (AWS, GCP, Azure) for AI/ML deployments.
- Experience in building key GTM ML models including MTA, predictive bookings, next best product, and churn predictions.
- Strong understanding of data governance, compliance, and regulatory reporting.
- Experience managing analytics functions in B2B SaaS, Financial Services, or Enterprise Applications.
\#LI\-CS1
\#LI\-Remote
Benefits of Working at CrowdStrike:
- Market leader in compensation and equity awards
- Comprehensive physical and mental wellness programs
- Competitive vacation and holidays for recharge
- Paid parental and adoption leaves
- Professional development opportunities for all employees regardless of level or role
- Employee Networks, geographic neighborhood groups, and volunteer opportunities to build connections
- Vibrant office culture with world class amenities
- Great Place to Work Certified™ across the globe
CrowdStrike is proud to be an equal opportunity employer. We are committed to fostering a culture of belonging where everyone is valued for who they are and empowered to succeed. We support veterans and individuals with disabilities through our affirmative action program.
CrowdStrike is committed to providing equal employment opportunity for all employees and applicants for employment. The Company does not discriminate in employment opportunities or practices on the basis of race, color, creed, ethnicity, religion, sex (including pregnancy or pregnancy\-related medical conditions), sexual orientation, gender identity, marital or family status, veteran status, age, national origin, ancestry, physical disability (including HIV and AIDS), mental disability, medical condition, genetic information, membership or activity in a local human rights commission, status with regard to public assistance, or any other characteristic protected by law. We base all employment decisions\-including recruitment, selection, training, compensation, benefits, discipline, promotions, transfers, lay\-offs, return from lay\-off, terminations and social/recreational programs\-on valid job requirements.
If you need assistance accessing or reviewing the information on this website or need help submitting an application for employment or requesting an accommodation, please contact us at [email protected] for further assistance.
Find out more about your rights as an applicant.
CrowdStrike participates in the E\-Verify program.
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Right to Work
CrowdStrike, Inc. is committed to fair and equitable compensation practices. Placement within the pay range is dependent on a variety of factors including, but not limited to, relevant work experience, skills, certifications, job level, supervisory status, and location. The base salary range for this position for all U.S. candidates is $125,000 \- $180,000 per year, with eligibility for bonuses, equity grants and a comprehensive benefits package that includes health insurance, 401k and paid time off.
For detailed information about the U.S. benefits package, please click here.
Expected Close Date of Job Posting is:08\-12\-2026
Salary Context
This $125K-$180K range is below the median for Data Scientist roles in our dataset (median: $160K across 245 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 4,133 AI roles we're tracking, Data Scientist positions make up 8% of the market. At CrowdStrike, 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 $198,000 based on 868 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($152K) sits 23% below the category median. Disclosed range: $125K to $180K.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
CrowdStrike AI Hiring
CrowdStrike has 8 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer, Data Engineer. Positions span Remote, US, CA, US. Compensation range: $180K - $290K.
Remote Work Context
Remote AI roles pay a median of $173,300 across 2,012 positions. About 14% of all AI roles offer remote work.
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 4,133 open positions tracked in our dataset. By seniority: 106 entry-level, 1,901 mid-level, 1,663 senior, and 463 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (583 positions). The remaining 3,532 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 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 4,133 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,865), Data Scientist (339), AI Software Engineer (313). 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 (106) are outnumbered by mid-level (1,901) and senior (1,663) 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 463 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (583 positions), with 3,532 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 $200,700. Top-quartile roles start at $254,000, and the 90th percentile reaches $307,500. 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 $274,200 median, while Prompt Engineer roles sit at $140,000. 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 (2,128 postings), Aws (1,324 postings), Azure (1,003 postings), Rag (916 postings), Gcp (817 postings), Pytorch (655 postings), Prompt Engineering (639 postings), Claude (571 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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