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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. Machine learning has been at the heart of Northbeam since day one: our full suite of measurement, spanning multi-touch attribution, MMM+, and incrementality, is built on it, along with laser-accurate first-party data. Now we're taking that foundation further, using AI to help brands operationalize their data and turn Northbeam insights into automated decisions.
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 physical offices in San Francisco and Los Angeles.
About the Role
We are seeking a Data Scientist as a foundational member of our new "Applied Measurement" team. In this role, you will help scale Northbeam's incrementality and measurement products directly with customers, to help customers answer some of their most important marketing measurement questions. You'll work at the intersection of data science, causal inference, predictive modeling, and customer problems, contributing across Northbeam's measurement products. This can include experimentation and incrementality products, Marketing Mix Modeling (MMM), and other predictive or machine learning products.
This is an applied Data Science role with direct exposure to real-world customer problems. You'll work closely with experienced Data Scientists and cross-functional partners across Media Strategy, Customer Success, Product, and Engineering. As you develop deeper product and methodological expertise, you'll have opportunities to contribute to new methodology, tooling, and scalable measurement capabilities. You will also act as a trusted technical partner to clients: explaining statistical methodology, diagnosing measurement issues in their specific account, and clearly communicating experimentation results to drive actionable recommendations.
This role emphasizes customer partnership and applied communication as much as statistical methodology and production rigor. The ideal candidate combines strong statistical foundations with a service-oriented mindset, and is comfortable moving between modeling, software development, and direct customer conversations.
Your Impact
- Partner with customers and internal teams to understand important marketing measurement questions and translate them into data science problems.
- Work with real-world customer data to ensure it is reliable and appropriate for modeling and measurement.
- Analyze model and experiment results, investigate unexpected outcomes, and identify potential data, modeling, or measurement issues.
- Translate complex statistical and modeling results into clear insights and actionable recommendations.
- Partner with Senior Data Scientists on more complex methodological and modeling problems. Collaborate with cross-functional peers to support clients with the practical application.
- Identify recurring challenges and opportunities to improve our models, diagnostics, tooling, and processes.
- Build reusable analysis and diagnostic tools that make measurement more reliable and scalable.
- As you grow in the role, contribute to the development of new methodologies, models, and production measurement capabilities.
What You Bring
- Bachelor's degree (MS preferred) in Computer Science, Statistics, Mathematics, Engineering, or another highly quantitative field.
- 2+ years of experience building and deploying data science, machine learning, or AI systems in production, ideally including direct client- or customer-facing work.
- Strong foundation in statistics, machine learning, experimentation, and causal inference, with genuine enthusiasm for explaining these methods to non-technical audiences.
- Excellent verbal and written communication skills—comfortable leading a customer conversation, explaining a modeling tradeoff to a non-technical stakeholder, and writing up findings clearly.
- Strong coding and debugging skills, with the ability to write production-quality code and diagnose issues across the full stack, including source data, pipelines, model logic, and customer-facing outputs.
- A service-oriented mindset: energized by direct, hands-on customer problem-solving, not only methodology for its own sake.
- Comfortable working in a fast-paced startup environment and driving projects from concept to production with a high degree of autonomy.
- Growth mindset, intellectual curiosity, and a willingness to learn new domains, customer verticals, and measurement methodologies.
- Strong preference for candidates with a robust statistical background and prior experience in digital marketing or eCommerce analytics.
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 $130K-$160K range is below the median for Data Scientist roles in our dataset (median: $167K across 150 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 8,943 AI roles we're tracking, Data Scientist positions make up 4% 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 $190,000 based on 1,406 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($145K) sits 24% below the category median. Disclosed range: $130K to $160K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Northbeam AI Hiring
Northbeam has 1 open AI role right now. They're hiring across Data Scientist. Based in Remote, US. Compensation range: $160K - $160K.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 positions. About 6% 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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