Interested in this Data Scientist role at Apollo.io?
Apply Now →Skills & Technologies
About This Role
Apollo.io is the leading go\-to\-market solution for revenue teams, trusted by over 500,000 companies and millions of users globally, from rapidly growing startups to some of the world's largest enterprises. Founded in 2015, the company is one of the fastest growing companies in SaaS, raising approximately $250 million to date and valued at $1\.6 billion. Apollo.io provides sales and marketing teams with easy access to verified contact data for over 210 million B2B contacts and 35 million companies worldwide, along with tools to engage and convert these contacts in one unified platform. By helping revenue professionals find the most accurate contact information and automating the outreach process, Apollo.io turns prospects into customers. Apollo raised a series D in 2023 and is backed by top\-tier investors, including Sequoia Capital, Bain Capital Ventures, and more, and counts the former President and COO of Hubspot, JD Sherman, among its board members.
Position Overview:
We are seeking a highly analytical and impact\-driven Senior Data Scientist to help shape our product strategy and decision\-making through data. In this role, you will focus on optimizing existing products while also supporting early\-stage product initiatives where relevant. You will work closely with cross\-functional teams to drive product innovation and optimization through data\-driven insights. This position requires a passion for transforming data into actionable insights and the ability to thrive in a fast\-paced environment.
What You’ll Do:
- Translate business needs into data analysis and actionable insights to optimize product performance and user experience for both existing and new products.
- Work closely with product managers, engineers, and other cross\-functional stakeholders to identify critical product questions and provide data\-driven solutions.
- Develop and maintain a scalable, reliable, and accurate analytics infrastructure to support product decisions.
- Establish and maintain best practices for data collection, analysis, and reporting.
- Deliver actionable insights and recommendations focused on landing tangible product impact.
- Help foster a culture of data\-driven decision\-making and continuous improvement within the product teams.
- Ensure the integrity and accuracy of the data used for analysis and reporting.
- While the primary focus is on existing products, support 0 to 1 product launches by identifying key metrics and analyzing early\-stage product data when applicable.
What You Bring:
- Strong experience in product analytics or a similar data\-focused role, with a proven ability to impact product strategy and decision\-making through data.
- Expertise in translating complex data into insights that are actionable for cross\-functional teams.
- Proficiency with data analytics tools and platforms (e.g., Looker, Amplitude) is a plus but not required. A focus on solving product challenges is more important than specific tool experience.
- Experience in building and maintaining reliable analytics infrastructures.
- Excellent communication skills, with the ability to clearly present data insights to both technical and non\-technical stakeholders.
- Experience with 0 to 1 product initiatives is a plus, though not required.
- A high level of accountability, a passion for continuous learning, and the ability to thrive in a fast\-paced, dynamic environment.
Qualifications:
- Degree in Analytics, Applied Mathematics, Economics, Statistics, or related analytical field of study, or an equivalent combination of training and experience.
- Deep experience in working with exploratory analysis projects related to cohorting, time series analysis, and funnel analysis/optimization is required.
- Expert\-level proficiency in influencing product strategy with SQL, A/B testing, machine learning, statistical analysis, and related tools like R, Python, SAS, etc., is required.
- Previous or current experience supporting SaaS (Product\-Led Growth) companies with uncovering gaps and recalibrating activation metrics (e.g., aha/habit moments) is a huge plus.
- Experience with Product Analytics tools to track end\-to\-end journeys/funnels (e.g., Amplitude, Heap, Mixpanel) and analyzing events within those products is essential.
- Proven ability to thrive in a fast\-paced, dynamic startup environment with a high level of adaptability and a strong product sense, ensuring insights align with customer needs and product goals.
The listed Pay Range reflects the total cash compensation inclusive of annual base salary and annual bonus as applicable. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonus target and annual base salary for the role. This salary range may be inclusive of several career levels at Apollo and will be narrowed during the interview process based on a number of factors, including the candidate’s experience, qualifications, and location. Applicants interested in this role who are not located in the US may request the annual salary range for their location during the interview process.
Additional benefits for this role may include: equity; company bonus or sales commissions/bonuses; 401(k) plan; at least 10 paid holidays per year, flex PTO, and parental leave; employee assistance program and wellbeing benefits; global travel coverage; life/AD\&D/STD/LTD insurance; FSA/HSA and medical, dental, and vision benefits.
Tier 1 Pay Range (San Francisco, New York City, Seattle)
$236,000 \- $295,000 USD
Tier 2 Pay Range (All other US Locations)
$205,200 \- $256,500 USD
### We are AI Native
Apollo.io is an AI\-native company built on a culture of continuous improvement. We’re on the front lines of driving productivity for our customers—and we expect the same mindset from our team. If you're energized by finding smarter, faster ways to get things done using AI and automation, you'll thrive here.
### Why You’ll Love Working at Apollo
At Apollo, we’re driven by a shared mission: to help our customers unlock their full revenue potential. That’s why we take extreme ownership of our work, move with focus and urgency, and learn voraciously to stay ahead.
We invest deeply in your growth, ensuring you have the resources, support, and autonomy to own your role and make a real impact. Collaboration is at our core—we’re all for one, meaning you’ll have a team across departments ready to help you succeed. We encourage bold ideas and courageous action, giving you the freedom to experiment, take smart risks, and drive big wins.
If you’re looking for a place where your work matters, where you can push boundaries, and where your career can thrive—Apollo is the place for you.
Salary Context
This $205K-$295K range is above the 75th percentile 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 Apollo.io, 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 ($250K) sits 26% above the category median. Disclosed range: $205K to $295K.
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.
Apollo.io AI Hiring
Apollo.io has 1 open AI role right now. They're hiring across Data Scientist. Based in Remote, US. Compensation range: $295K - $295K.
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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.