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About This Role
SpyCloud is on a mission to make the internet a safer place by disrupting the criminal underground. SpyCloud’s solutions thwart cyberattacks and protect more than 4 billion accounts worldwide. Cybersecurity is an exciting, evolving space, and being at the forefront of the fight to disrupt cybercrime makes SpyCloud a special place to work. If you’re driven to align your career with a fantastic mission, look no further!
We're looking for a Senior Data Scientist, Applied ML to design, build, and deploy models for critical cybersecurity use cases like incident detection and mitigation, fraud intelligence, and risk scoring.
You'll own the full model lifecycle — from data understanding and preparation through prototyping and deployment in production — and work closely with engineering, product, and research teams to turn complex problems into scalable, reliable systems. This role is ideal for someone who thrives in applied, hands\-on environments where impact and collaboration matter, and who has genuinely owned data work end\-to\-end.
What You'll Do:
You will develop, train, and deploy models using real\-world structured and unstructured data to power critical security features such as threat detection and alerting, entity resolution and risk scoring, and natural language\-based tagging and classification. You'll build the preprocessing and feature engineering pipelines your own models depend on, and you'll own model monitoring and evaluation, designing feedback loops to continuously improve accuracy and effectiveness.
You'll be equally comfortable prototyping new approaches from scratch and taking existing prototypes — from our R\&D team or your own experimentation — to production\-grade reliability. This role sits deliberately at the intersection of research and deployment, not on one side of it: you'll take ownership of data validation, transformation, and pipeline health across the handoff points between research and production, not just within the boundaries of your own models.
Working closely with software and data engineers, you'll help productionize models in modern cloud\-native environments like AWS.
This role is highly collaborative. You'll partner with product managers and domain experts to define success criteria, rapidly prototype MVPs to test new features or signals, and work with the data engineering team to access and understand diverse data sources, owning the transformation and validation steps throughout. Your input will also contribute to broader system design and architectural decisions.
Strong communication and documentation skills are essential. You will clearly articulate model design choices, tradeoffs, and outcomes to both technical and non\-technical stakeholders, maintain thorough documentation for models, pipelines, and evaluation methodologies, and participate in model and compliance reviews and customer\-facing discussions as needed.
Requirements:
- 4\+ years of experience building and shipping models in production with direct, hands\-on ownership of the data lifecycle around them
- Strong background in applied math (linear algebra, optimization, statistics) and machine learning
- Demonstrated experience leveraging Natural Language Processing (NLP) techniques for text classification, tagging, or entity extraction
- Proficiency in Python and key ML libraries: PyTorch, TensorFlow, scikit\-learn, XGBoost
- Demonstrated experience building or maintaining data/feature pipelines (e.g., with Airflow, Spark, Pandas) as part of your own modeling work
- Comfort with model versioning and monitoring in production (e.g., MLflow, DVC)
- Working experience deploying models into cloud environments or containerized services
- Strong communication skills and the ability to translate complex problems into actionable solutions
Nice to Have:
- Deeper MLOps/DevOps/data engineering exposure: infra\-as\-code, CI/CD depth, etc.
- Familiarity with cybersecurity datasets or domains: threat intelligence, account takeover, ransomware, etc.
- Exposure to graph analytics, knowledge graphs, or cybersecurity frameworks like MITRE ATT\&CK
- Background working with unstructured data (e.g., log files, threat reports, breach datasets)
Base Salary Range: $154,000 – $200,000
The salary range reflects the expected base compensation for a fully qualified candidate at this level based on experience, qualifications, and market data at the time of posting.
U.S.\-Based Benefits \+ Perks (for Full Time Employees):
At SpyCloud, we are committed to working alongside individuals who are equally passionate about preventing cybercrime, regardless of their department or role. Guided by our core values in all business decisions, we prioritize unity in our mission and ensure all SpyCloud employees have the support and benefits they need to stay focused on our goals. In addition to our engaging workspace in South Austin, flexible and remote\-friendly work options, and competitive salary package, we offer our employees a comprehensive benefits package that includes:
- 401(k) with Employer Contribution
- Health, Vision, and Dental Insurance
+ Health Savings Account (HSA) available with Employer Contribution
- Employer Paid Life, Short\-term, and Long\-term Disability Insurance
- Generous PTO Plan and 16 paid holidays per year
U.K.\-Based Benefits \+ Perks (for Full Time Employees):
- Retirement Savings Plan with Employer Contribution
- Employer Provided Private Health Insurance and Healthcare Cashplan
- Employer Paid Life Insurance and Income Replacement
- Generous Holiday Plan and 14 paid holidays per year
About SpyCloud:
SpyCloud transforms recaptured darknet data to disrupt cybercrime. Its automated identity threat protection solutions use advanced analytics and AI to accelerate investigations and protect workforce, consumer, and supplier identities from the threats that matter most: authentication bypass, session hijacking, malicious insiders, account takeover, ransomware, and fraud. Its data from malware\-infected devices, successful phishes, combolists, and third\-party breaches also powers many popular dark web monitoring and identity theft protection offerings. Customers include 7 of the Fortune 10, along with hundreds of global enterprises, mid\-sized companies, and government agencies worldwide. Headquartered in Austin, TX, SpyCloud is home to more than 250 cybersecurity experts whose mission is to protect businesses and consumers from the stolen identity data criminals are using to target them now.
To learn more and see insights on your company's exposed data, visit spycloud.com.
Our Mission:
Our mission is to make the internet a safer place by disrupting the criminal underground. Together with our customers and partners, we aim to end criminals’ ability to profit from stolen information.
Who We Are:
SpyCloud is a place for innovative, collaborative, and problem\-solvers to thrive. Individually, we’re amazing, but together, we’re unstoppable. We celebrate diversity and various perspectives and aim to create an inclusive and supportive environment for all. We are proud to be an Equal Employment Opportunity and Affirmative Action employer of choice. All aspects of employment decisions will be based on merit, performance, and business needs. We do not discriminate on the basis of any status protected under federal, state, or local law. All qualified applicants will receive consideration for employment without regard to race, religion, color, national origin, sex (including pregnancy, childbirth, reproductive health decisions, or related medical conditions), sexual orientation, gender identity, gender expression, age, status as a protected veteran, status as an individual with a disability, genetic information, political views or activity, or other applicable legally protected characteristics. Women, minorities, individuals with disabilities, and protected veterans are encouraged to apply. SpyCloud complies with applicable state and local laws governing nondiscrimination in employment. This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation, and training.
SpyCloud expressly prohibits any form of workplace harassment. Improper interference with the ability of SpyCloud's employees to perform their job duties may result in discipline up to and including discharge. SpyCloud shares the right to work and participates in the E\-Verify program in all locations.
If you need assistance or accommodation due to a disability, you may contact us.
Our Culture:
Our culture is something really special. We’re all driven to disrupt the cybercriminal economy as we keep customer accounts safe from compromise. We support a truly worthy and serious mission, but we have fun doing it together. If you are driven, inventive, and collaborative, you’ll fit right in.
SpyCloud’s Recruitment Policy:
We will never ask an applicant for sensitive or personal financial information during the recruitment process. We advise all applicants seeking employment with SpyCloud to review available information on recruitment fraud. Anyone who suspects that they have been contacted by someone falsely representing SpyCloud should email [email protected].
Compensation Transparency Policy:
At SpyCloud, we believe in transparency and fairness in compensation. We strive to ensure that all employees are fairly compensated for their contributions, and we openly discuss our compensation philosophy and structure. We are committed to providing competitive salaries and benefits packages to attract and retain top talent, and we encourage open dialogue and feedback regarding compensation matters.
Learn more and apply: SpyCloud Careers
SpyCloud is not sponsoring visas at this time.
Salary Context
This $154K-$200K 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 SpyCloud, 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. This role's midpoint ($177K) sits 8% below the category median. Disclosed range: $154K to $200K.
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.
SpyCloud AI Hiring
SpyCloud has 1 open AI role right now. They're hiring across Data Scientist. Based in Austin, TX, US. Compensation range: $200K - $200K.
Location Context
AI roles in Austin pay a median of $214,343 across 143 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.
Frequently Asked Questions
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