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About This Role
Company at a Glance
OpenX is focused on unleashing the full economic potential of digital media companies. We do this by making digital advertising markets and technologies that are designed to deliver optimal value to publishers and advertisers on every ad served across all screens.
At OpenX, we have built a team that is uniquely experienced in designing and operating high\-scale ad marketplaces, and we are constantly on the lookout for thoughtful, creative executors who are as fascinated as we are about finding new ways to apply a blend of market design, technical innovation, operational excellence, and empathetic partner service to the frontiers of digital advertising.
A Data Scientist III is a proficient, fully independent scientist who owns medium\-to\-large data science projects end\-to\-end — from problem formulation and research through to deploying and maintaining production models. In this role, you will build production\-ready models and analyses that solve real marketplace problems, partner with product and engineering to ship them, mentor junior scientists, and act as a strong technical voice within your team.
Problems at this level include bidding and yield modeling, relevance and prediction systems at exchange scale, experimentation and causal measurement of marketplace changes, and the feature engineering, validation, and monitoring required to run ML reliably in production.
The ideal candidate brings a solid applied machine learning foundation, growing judgment in selecting methods for business problems at scale, and a track record of carrying analytical work from an ambiguous question through to measurable production impact.
### Key Responsibilities:
- Modeling \& Technical Execution:
+ Own the end\-to\-end data science lifecycle for moderately complex models and significant project components — spanning data ingestion, feature engineering, modeling, validation, deployment, monitoring, and retraining.
+ Apply expertise across several core areas of machine learning and statistics (e.g., gradient\-boosted models, deep neural networks, time series, causal inference concepts, experimentation design), selecting appropriate methods for complex data science problems.
+ Write efficient, modular, well\-tested code for data processing, feature engineering, and model training/inference, leveraging distributed tooling (e.g., Vertex AI pipelines, Dataflow, BigQuery) where appropriate.
+ Design and implement robust validation frameworks for complex experiments and models, accounting for potential biases and real\-world performance.
Troubleshoot complex model performance issues, data anomalies, and code bugs effectively with little guidance.
+
- Execution \& Collaboration:
+ Define analytical approaches and scope data science projects for moderately complex or ambiguous business problems.
+ Partner with product managers and stakeholders to define success metrics and experiment goals, and to translate marketplace problems into data science solutions.
+ Lead the design and analysis of experiments (e.g., A/B tests, switchback) for your projects, and interpret complex model results and experimental outcomes with a focus on actionable insights and business outcomes.
+ Proactively identify opportunities within your domain where data science can provide significant value, and initiate exploration.
+ Follow and help improve established team processes for coding standards, documentation, reproducibility, and experimentation.
- Mentorship \& Influence:
+ Mentor DS I and DS II scientists, providing technical guidance, reviewing code, analyses, and models, and supporting their growth in analytical and modeling skills.
+ Influence technical decisions within the team regarding modeling choices, validation strategies, and tooling through well\-reasoned arguments and expertise.
+ Drive improvements to team standards, data science best practices, and analytical rigor; take ownership of specific team practices or technical components (e.g., a feature store component, leading experimentation reviews).
+ Educate stakeholders on the capabilities and limitations of data science models, and clearly explain complex methodologies and findings to both technical and non\-technical audiences.
+ Participate actively in recruiting, providing high\-quality, graded interview feedback for candidates up to this level.
### Required Qualifications:
- B.S. or M.S. in Data Science, Machine Learning, Computer Science, Physics, Mathematics, Operations Research, or a related technical field with 5\+ years of relevant industry experience; OR a Ph.D. in a related field with 2\+ years of relevant experience.
- Demonstrated ability to independently own the full data science lifecycle — from problem formulation and feature engineering through model deployment, monitoring, and ongoing maintenance.
- Solid expertise in several core areas of machine learning and/or statistics (e.g., gradient\-boosted models, deep neural networks, time series, causal inference, experimentation design), with the judgment to select appropriate methods for complex problems.
- Strong foundation in probability and statistics, including techniques that scale to large datasets.
- Experience designing and analyzing experiments (e.g., A/B testing) and building robust model and experiment validation frameworks.
- Strong Python and SQL skills; experience with ML frameworks such as TensorFlow or PyTorch.
- Ability to write efficient, modular, well\-tested code and to collaborate with engineering to move models and analyses into production.
- Strong communication skills, including the ability to convey complex technical concepts to both technical and non\-technical audiences.
### Desired Characteristics:
- Experience developing, evaluating, or optimizing models or bidding algorithms for RTB environments.
- Experience working with a cloud platform like GCP/AWS/Azure, with emphasis on GCP and the Vertex AI platform.
- Experience with ML pipeline and orchestration tools such as TFX, Kubeflow, or Airflow.
Familiarity with other programming languages such as Java and Go.
- Experience working in digital media, marketing technology, or advertising technology, especially in marketplace, auction, or exchange systems.
- Experience supporting and improving production ML models beyond their initial deployment.
- Experience mentoring junior data scientists.
Pursuant to any state, local ordinance, or local hiring regulations, we will consider for employment any qualified applicant, including those with arrest and conviction records, in a manner consistent with the applicable regulation.
OpenX is committed to fair and equitable compensation practices. For all applicants, the base salary range is noted above, per year \+ bonus \+ equity \+ benefits. A candidate’s salary is determined by various factors including, but not limited to, relevant work experience, skills, and certifications.
A summary of our benefits, which include medical, dental, vision, 401k, equity and more, can be viewed here: https://www.openx.com/company/careers/ A candidate’s salary is determined by various factors including, but not limited to, relevant work experience, skills, and certifications.
OpenX VALUES
Our five company values form a solid bedrock serving to define us as a group and guide the company. Our values remind us that how we do things often matters as much as what we do. WE ARE ONE
We are one team. There are no exceptions. We are a group of strong and diverse individuals unified by a shared mission. We embrace challenges and win together as a team. We respect and care about our colleagues and cultivate an inclusive culture WE ARE CUSTOMER CENTRIC
We innovate on behalf of our customers. We understand, respect, and listen carefully to our customers. We build great products to solve our customers’ problems. We manage our customers’ expectations clearly and honestly. We are a trusted partner to all of our customers \- we act with integrity at all times. We care. OPENX IS OURS
We are all owners of OpenX
We all have a voice to improve OpenX
We stake our personal and professional reputations on the excellence of our work
We are not interested in just "doing our jobs"; we take ownership to drive results WE ARE AN OPEN BOOK
We understand and respect what each of us does. We are eager to teach and share what we know with others, both internally and externally. We are eager to learn from others and we ask questions internally and externally. WE EVOLVE FAST
We take responsible risks and own and learn from our mistakes. We recognize and repeat success. We actively seek out and provide constructive feedback. We adapt quickly and embrace change. We tackle growth and learning with real urgency. We are endlessly curious.
OpenX TRAITS
Our three traits capture what makes a great team member at OpenX. HUMBLE
Ideal team players are humble and demonstrate integrity. They put the team's success above their own, share credit generously, and value collective achievements. They are self\-assured, open to coaching, and committed to continuous learning. DRIVEN
Ideal team players are results\-driven and motivated. They are curious, always seeking more to do, learn, and take on. As proactive problem\-solvers, they take initiative without needing external motivation. They continuously think about the next steps and opportunities for improvement. SMART
Ideal team players are smart and possess the intellectual acumen to understand the complexities of our organization and industry. They are interpersonally intelligent, good communicators, and exemplify sound judgment in their interactions across the company to foster a collaborative environment. OpenX is committed to equal employment opportunities.It is a fundamental principle at OpenX not to discriminate against employees or applicants for employment on any legally\-recognized basis including, but not limited to: age, race, creed, color, religion, national origin, sexual orientation, sex, disability, predisposing genetic characteristics, genetic information, military or veteran status, marital status, gender identity/transgender status, pregnancy, childbirth or related medical condition, and other protected characteristic as established by law. OpenX Applicant Privacy Policy
Applicants can review our Applicant Privacy Policy at any time by visiting the following link: https://www.openx.com/privacy\-center/applicant\-privacy\-policy/. Effective Date: November 21, 2024
Salary Context
This $143K-$160K range is below the median 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 OpenX, 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 ($152K) sits 23% below the category median. Disclosed range: $143K to $160K.
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.
OpenX AI Hiring
OpenX has 1 open AI role right now. They're hiring across Data Scientist. Based in New York, NY, US. Compensation range: $160K - $160K.
Location Context
AI roles in New York pay a median of $211,000 across 2,643 tracked positions. That's 5% 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 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.
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