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About This Role
Job Title: Senior Data Scientist - Measurement
Location: Remote - US Only
About InMarket
Since 2010, InMarket has been the leader in 360-degree consumer intelligence and real-time activation for thousands of today's top brands. Through InMarket's data-driven marketing platform, brands can build targeted audiences, activate media in real time, and measure success in driving return on ad spend. InMarket's proprietary Moments offering outperforms traditional mobile advertising by 6x.* Our LCI attribution platform, which won the MarTech Breakthrough Award for Best Advertising Measurement Platform, was validated by Forrester to drive an average of $40 ROAS for our clients.
- Source: Wordstream US Google Display Benchmarks for Mobile Media
About the Role
AI Engineering is a core driver of InMarket's product design and innovation. As an AI Engineering team member, you will have the opportunity to leverage cutting-edge data science, machine learning and AI on petabytes of data in ways that make a large and lasting impact on the company. We take on greenfield projects and launch entirely new products giving you the opportunity to take large amounts of ownership and shape our technology stack.
Your role is to conceptualize, develop and deploy the core machine learning and AI solutions that power our industry-leading advertising measurement products. These solutions will be intelligent, adaptive, operate at scale, and be based on modern causal inference, marketing statistical methods and AI. You will collaborate closely with data analysts, account managers, product teams and engineering to understand our business and rapidly iterate on solutions that maximize ROI. We're an AI-forward organization, and agentic development is core to how we work - AI Engineering is constantly experimenting with AI tools to move from idea to production faster than ever.
Your daily Impact as Senior Data Scientist
- Work with product stakeholders to develop prototypes and take early-stage ideas through to production
- Take ownership of core advertising measurement products to expand our capabilities and push our performance boundaries
- Explore our data and systems, prototype ideas, run experiments to validate them and propose new products or advancements
- Collaborate closely with our engineering and product teams to expand our causal inference and attribution based measurement platform
Your Experience & Expertise
- Bachelors in Statistics, Computer Science, Economics, Operations Research, or similar quantitative field (graduate degree preferred)
- Solid understanding of modern data science, AI and machine learning
- At least 5 years of experience with applying modern statistical marketing models, Bayesian statistics, and causal inference to solve complex, data-driven problems
- Programming proficiency (preferably Python, its data science stack, and SQL) and desire to write production code
- Excellent communication skills and the ability to present methodologies and findings to audiences with varying technical backgrounds
- Proven track record of owning projects and outcomes from research and prototyping through to production deployment.
- Experience using Claude Code, Codex or similar
- Experience with agentic development
Nice to Haves
- Experience building and supporting Media Mix Modeling (MMM)
- Experience working in modern cloud infrastructure environments like AWS and GCP is a big plus - particularly, GCP.
- Experience with BigQuery
- Familiarity with the digital media / advertising industry is a big plus.
- Experience with Spark
Compensation & Benefits Summary
- Competitive salary, stock options, flexible vacation
- Medical, dental and Flexible Spending Account (FSA)
- Company Matched 401(k)
- Unlimited PTO (Within reason)
- Talented co-workers and management
- Agile Development Program (For continued learning/professional development)
- Paid Paternity & Maternity Leave
For candidates in California, Colorado, New York City, Illinois, Minnesota, New Jersey, Vermont, and Massachusetts, the Targeted Base Salary Range for this role is $160,000 - $195,000.
Actual salaries will vary depending on factors including but not limited to work experience, specialized skills and training, performance in role, business needs, and job requirements. Base salary is subject to change and may be modified in the future. Base salary is just one component of InMarket's total rewards package that also may include bonus, equity, and benefits. Ask your recruiter for more information!
At InMarket we are committed to a culture that supports diversity, inclusion, belonging and equal opportunity. We celebrate all people and believe everyone deserves respect regardless of race, gender, sexual orientation, backgrounds, experiences, abilities or beliefs.
InMarket is an Equal Opportunity Employer (EOE). Qualified applicants are considered for employment without regard to age, race, color, religion, sex, national origin, sexual orientation, disability, or veteran status.
Privacy Notice for California Job Applicants: https://inmarket.com/ca-notice-for-job-applicants/
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Salary Context
This $160K-$195K range is above the median for Data Scientist roles in our dataset (median: $162K across 195 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 11,520 AI roles we're tracking, Data Scientist positions make up 4% of the market. At InMarket, 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 $190,000 based on 1,564 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($177K) sits 7% below the category median. Disclosed range: $160K to $195K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
InMarket AI Hiring
InMarket has 2 open AI roles right now. They're hiring across Data Scientist. Based in New York, NY, US. Compensation range: $155K - $195K.
Location Context
AI roles in New York pay a median of $221,000 across 3,257 tracked positions. That's 4% 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 postings), Kubernetes (559 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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