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About This Role
Amplitude is the leading AI analytics platform, helping over 4,700 customers—including Atlassian, Burger King, NBCUniversal, and Square—build better products and digital experiences. With powerful AI Agents embedded across our platform, teams can analyze, test, and optimize user experiences faster than ever. Ranked \#1 across multiple categories in G2’s Winter 2026 Report, Amplitude is the best\-in\-class solution for product, data, and marketing teams. Learn more at amplitude.com.
As an organization, we deliver for our customers by living our values. We operate from a place of humility, take ownership of problems and successes, approach challenges with a growth mindset, and put our customers at the center of everything we do.
Amplitude’s Commitment to Diversity Equity \& Inclusion (DEI): Amplitude believes that diversity enables the creation of better products, improves the ability to solve complex problems, and drives more powerful solutions. We strive to create an environment of inclusion—one focused on psychological safety, empathy, and human connection—that will allow employees of all backgrounds to thrive.
About The Role \& Team
Amplitude is on a mission to help companies build better products through data. Following our acquisition of Statsig, we now offer one of the most comprehensive experimentation and product analytics platforms on the market. As a Customer Data Scientist, you will combine deep experimentation expertise, applied statistics, and customer\-facing technical consulting to help some of the world's most sophisticated product and engineering organizations evaluate, adopt, and scale modern experimentation programs.
This is a highly strategic and technical role that partners closely with Sales, Solutions Engineering, Product, and Engineering teams. You will serve as the trusted expert on experimentation methodology, statistical rigor, and experimentation infrastructure, helping customers solve complex challenges while influencing the future direction of the Amplitude and Statsig platforms.
As a Customer Data Scientist, you will:
- Serve as the primary experimentation expert throughout customer evaluations, guiding technical discussions from discovery through proof of concept.
- Partner directly with data scientists, engineers, and technical leaders to advise on experiment design, statistical methodology, measurement frameworks, and experimentation architecture.
- Help customers build scalable and trustworthy experimentation programs by educating teams on best practices in experimentation, metric development, variance reduction, and results interpretation.
- Collaborate closely with Account Executives, Solutions Engineers, Product Managers, and Engineers to accelerate customer success and bring valuable field insights back into product development.
- Create repeatable frameworks, technical content, and enablement materials that strengthen experimentation expertise across customers and internal teams.
You'll be a great addition to the team if you have:
- You have built, scaled, or supported experimentation programs within a product\-led technology company and understand both the technical and organizational challenges involved.
- You have experience working with experimentation platforms such as Statsig, Eppo, Optimizely, LaunchDarkly, or similar technologies.
- You are comfortable engaging with highly technical audiences and can effectively communicate complex statistical concepts to data scientists, engineers, executives, and business stakeholders.
- You enjoy translating technical depth into practical business outcomes and helping organizations make better product decisions through experimentation.
- You have experience influencing product strategy, technical roadmaps, or best practices based on customer needs and market feedback.
At a minimum, you need to have:
- 5\+ years of experience in Data Science, Experimentation, Analytics, Applied Statistics, or a related field with a strong foundation in experimental design and causal inference.
- Hands\-on experience designing, running, and analyzing A/B tests and applying advanced experimentation methodologies, including variance reduction, holdouts, sequential testing, or related techniques.
- Strong proficiency in SQL and python and experience working with cloud data warehouses such as Snowflake, BigQuery, or Redshift.
- Experience using Python or R for statistical analysis, experimentation, and data modeling.
- Experience working directly with customers, stakeholders, or cross\-functional partners to solve complex technical challenges and communicate sophisticated concepts effectively.
### Our values:
At Amplitude, our values guide how we show up for one another and for our customers:
- Humility: We operate from a place of empathy and openness, seeking to understand many points of view.
- Ownership: We take the initiative to solve problems that drive our shared company success.
- Growth Mindset: We’re tenacious in the face of challenges and seek feedback in order to grow ourselves and others.
- Customer Centricity: We put the customer at the center of everything we do and are deeply committed to their success.
We care about the well\-being of our team: We offer competitive pay and benefits packages that reflect our commitment to the health and well\-being of our Ampliteers.
Some of our benefit programs include:
- Excellent Medical, Dental and Vision insurance coverages, with 100% employer\-paid premiums for employee Medical, Dental, Vision on select plans
- 401(k) retirement plan with an employer match of up to 1% of your eligible pay each pay period up to $2,000 annually
- Flexible time off, paid holidays, and more
- Generous stipends to spend on what matters most to you, whether that’s wellness (monthly), commuter transit/parking (monthly), learning and development (quarterly), new hire home office equipment, and much more
- Excellent Parental benefits including: 12 weeks of Paid Parental Leave, Carrot Fertility Benefits/Adoption/Surrogacy support, Back\-up Child Care support
- Mental health and wellness benefits including no cost employee access to Modern Health coaching \& therapy sessions
- Employee Stock Purchase Program (ESPP)
Other fun facts about Amplitude:
- Our customers love us! They've said we're the \#1 product analytics solution for 23 quarters in a row on G2\.
- We care a lot about product innovation. We've made significant investments in talent and infrastructure to build the most powerful AI analytics platform on the market.
- We invest in our people. We offer mentorship programs, management training, and wellness initiatives.
- We give back to our communities. We give every Ampliteer a charitable giving grant and paid volunteer time off.
- We were founded in 2012, went public via a direct listing in September 2021, and are now trading under the ticker $AMPL.
- We’re a global and fast\-growing team! We have employees around the world and offices in San Francisco (HQ), New York, Vancouver, Amsterdam, London, Paris, Singapore, and Tokyo.
- Our mascot is Data Monster, who loves to chow down on numbers, charts, and graphs. Nom nom.
Amplitude provides equal employment opportunities (EEO). All applicants are considered without regards to race, color, religion, national origin, age, sex, marital status, ancestry, physical or mental disability, veteran status, or sexual orientation.
By applying for this job, you acknowledge that Amplitude processes your personal data in accordance with the
*Staying Safe \- Protect Yourself From Recruitment Fraud*
*We are aware of individuals and entities fraudulently representing themselves as Amplitude recruiters and/or hiring managers. Amplitude will never ask for financial information or payment, or for personal information such as bank account number or social security number during the job application or interview process. Any emails from the Amplitude recruiting team will come from an @**amplitude.com* *email address.* *Please exercise caution and cease communications if something feels suspicious about your interactions.*
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 Amplitude, 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 808 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000.
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.
Amplitude AI Hiring
Amplitude has 1 open AI role right now. They're hiring across Data Scientist. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $170,000 across 1,926 positions. About 15% 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 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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