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### About Northbeam
Northbeam is building the world's most advanced marketing intelligence platform, providing top eCommerce brands a unified view of their business data through powerful attribution modeling and customizable dashboards. Our technology helps customers accurately track ad spend, understand the full customer journey, and drive profitable growth.
We're experiencing rapid growth, have strong product\-market fit, and are looking for the right people to help us scale. This is a rare chance to make a meaningful impact at a fast\-moving, high\-growth company. At Northbeam, you'll join a team of driven, collaborative, and talented individuals who value personal growth and excellence. We'd love for you to be part of our journey.
We're a remote\-friendly company with offices in San Francisco and Los Angeles.
### About the Role
We are seeking a Senior Data Scientist to help build and scale Northbeam's measurement products, including MMM, Incrementality, Insights, and Recommendation Systems.
This is an applied data science role focused on translating statistical and causal inference methodologies into reliable, production\-grade systems. You will work across the full lifecycle of measurement products—from methodology evaluation and model development to implementation, deployment, monitoring, and customer support.
This role emphasizes production implementation and operational ownership as much as statistical methodology.
The ideal candidate combines strong statistical foundations with a builder mindset and is comfortable moving between modeling, software development, production operations, and customer\-facing problem solving.
### Your Impact
- Build, deploy, and maintain production systems that power Northbeam's measurement products.
- Translate statistical and causal inference methodologies into scalable, reliable customer\-facing capabilities.
- Improve the accuracy, coverage, reliability, and operational robustness of our measurement systems.
- Debug data, modeling, and production issues across the full stack, from source data to customer\-facing outputs.
- Evaluate methodological improvements pragmatically, balancing statistical rigor, implementation complexity, maintainability, and business value.
- Partner closely with teammates across Data Science, Engineering, Product, and Customer Success to improve measurement quality and product capabilities.
- Explain statistical concepts to customers, troubleshoot measurement issues, and gather feedback to improve Northbeam's products.
### What You Bring
- Bachelor's degree (MS or PhD preferred) in Computer Science, Statistics, Mathematics, Engineering, or another highly quantitative field.
- 5\+ years of experience building and deploying data science, machine learning, or AI systems in production environments.
- Strong foundation in statistics, machine learning, experimentation, and causal inference, with experience applying these methods to solve real\-world business problems.
- Strong coding and debugging skills, with the ability to write production\-quality code that is maintainable, testable, and reliable.
- Experience implementing, deploying, and supporting statistical or machine learning systems in production environments.
- Ability to debug issues across the full stack, including source data, data pipelines, model logic, production services, and customer\-facing outputs.
- Experience owning projects end\-to\-end, from problem definition and methodology evaluation through production deployment and operational support.
- Comfortable working in a fast\-paced startup environment and driving projects from concept to production with a high degree of autonomy.
- Strong communication skills and the ability to explain technical concepts to both technical and non\-technical audiences.
- Growth mindset, intellectual curiosity, and a willingness to learn new domains, technologies, and measurement methodologies.
Actual compensation may vary based on experience, skills, and location.
In addition to your base salary, we offer an equity package, comprehensive healthcare benefits (medical, dental, and vision), and a 401(k) plan. Our team enjoys a flexible PTO policy, 12 company\-paid holidays, and 12 weeks of paid parental leave. We also provide a $500 work\-from\-home stipend to support your remote setup.
Interview Process
The interview process varies by role but typically begins with a 30\-minute interview with a Northbeam recruiter, followed by a video interview with the hiring manager. Next, candidates complete a role\-specific video interview followed by video or onsite interviews with several team members. The final step is a video interview with our CEO/Co\-founder. The entire interview process is usually 5\-7 interviews total and requires around 5\-8 hours of your time.
We accept applications on an ongoing basis.
Salary Context
This $170K-$200K range is above the median for Data Scientist roles in our dataset (median: $157K across 236 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 3,823 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Northbeam, 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 in Demand for This Role
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 808 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($185K) sits 7% below the category median. Disclosed range: $170K to $200K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Northbeam AI Hiring
Northbeam has 1 open AI role right now. They're hiring across Data Scientist. Based in San Francisco, CA, US. Compensation range: $200K - $200K.
Location Context
AI roles in San Francisco pay a median of $253,000 across 2,168 tracked positions. That's 26% above the national 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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