Interested in this Data Scientist role at ISPOT?
Apply Now →Skills & Technologies
About This Role
Immigration / Work Authorization Notice:Applicants must be currently authorized to work in the United States. iSpot is not able to sponsor or take over sponsorship of an employment visa for this position at this time.
iSpot competes for the best talent. Our compensation packages consist of salary and equity in one of Seattle’s hottest start\-ups, as well as other standard benefits. Most importantly, we provide a really interesting working experience, and the chance to contribute to the success of something great.
About iSpot:
At iSpot, we empower advertisers to measure the true impact of their TV and streaming campaigns—from concept through airing to conversion. Our fast, accurate, and actionable measurement and attribution solutions enable advertisers to evaluate creative effectiveness, optimize media plans, and attribute results across platforms, all while benchmarking against competitors and historical data. With real\-time performance insights seamlessly integrated across linear and streaming TV, iSpot equips advertisers to make swift, confident decisions that drive impactful business results.
About the Role:
We are seeking a Data Scientist to join our team. In this role, you will analyze, manipulate, clean, and process large and messy data using predictive modeling solutions and cloud\-based computing resources/infrastructure to scale and optimize data science tools and pipelines.
Responsibilities:
You will be responsible for a variety of critical tasks, including:
- Implement industry\-standard predictive modeling solutions.
- Analyze, manipulate, clean, and process large and messy data using predictive modeling solutions and cloud\-based computing resources/infrastructure to scale and optimize data science tools and pipelines.
- Code original R/Python scripts, optimize current scripts, and develop analytical reports and models.
- Analyze and improve data methods and efficiencies across the team, including designing and implementing wrapper/tool/utility functions for automated tasks to be used by less\-technical Analytics team members, and organize them into distributable, regularly maintained, and tested R/Python packages.
- Work independently or collaboratively on large and complex data science problems and analysis, including working with internal stakeholders and clients to develop analytical solutions within client context.
- Lead technical portion of client projects throughout the duration of their flight.
- Provide technical data science expertise to assist sales teams to build new business opportunities.
- Conduct in\-depth research to improve multi\-source data quality.
Qualifications:
- Master’s degree or foreign equivalent in Data Science, Mathematics, Statistics, or related quantitative field. 3\.5 G.PA. or higher or foreign equivalent, including graduate coursework in:
+ Probability and Statistics for Data Science
+ Computational Linear Algebra
+ Natural Language Processing
+ Deep Learning
+ Big Data, and
+ Machine Learning
- 3 months of experience with the following:
+ Analyzing and manipulating large and messy or complex data
+ Python, PySpark or Spark
+ SQL;
+ adapting/applying, and scaling of techniques in machine learning, deep learning, statistical analysis, reinforcement learning and/or time\-series analysis;
+ data visualization tasks; and
+ ggplot2, Matplotlib, and/or Shiny.
- Demonstrated knowledge of Git command line or similar GitHub tools as shown by undergraduate/graduate coursework or experience.
Salary Range: $112,807 to $142,010 USD annually
We are committed to providing competitive, market\-informed compensation. The cash compensation above includes base salary, variable commission for employees in eligible roles, and annual bonus targets for eligible roles. In addition to cash compensation, all full time iSpotters are eligible to participate in iSpot’s equity plan to receive stock options. Non\-exempt roles will also be eligible for (pre\-approved) overtime pay. Individual compensation packages are influenced by different factors unique to each candidate, including their skills, experience, qualifications and other job\-related reasons.
Flexible Workplace Policy:
iSpot supports a hybrid and flexible workplace. This position is designated to work at our headquarter location in Bellevue, WA, coming in 1\-3 days a week, or fully remote at a home office located anywhere in the U.S.
If you don't feel you met every single requirement for the role, don't rule yourself out. Please apply anyway!
iSpot is an equal opportunity employer. All applicants will receive consideration for employment without regard to race, ethnicity, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. If you need assistance and/or a reasonable accommodation due to a disability during the application or the recruiting process, please contact our HR team.
Salary Context
This $112K-$142K range is in the lower quartile 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 ISPOT, 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. This role's midpoint ($127K) sits 36% below the category median. Disclosed range: $112K to $142K.
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.
ISPOT AI Hiring
ISPOT has 1 open AI role right now. They're hiring across Data Scientist. Based in Remote, US. Compensation range: $142K - $142K.
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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.