Staff Data Scientist

JWX

About The Position

JWX’s mission is to provide technology that empowers media businesses to connect their content with consumers across every platform. We help publishers transform content into multi-format experiences, reach audiences wherever attention moves, and strengthen monetization in a fragmented landscape. As part of the broader ecosystem, JWX also supports streaming companies and advertisers with solutions built for how modern media is distributed and consumed. Our video players are embedded directly on publisher sites, including Accuweather, Axel Springer, Dotdash Meredith, McClatchy, Penske Media Co., and more. We work with leading brands and agencies including American Express, Citi, Dentsu, Mars, UM, and more helping them target ads against our publishers’ video content. We power streaming for over 2,000 blue-chip media companies, including 80% of the top 25 Comscore US publishers. As the largest independent global video network across CTV and OLV, we reach over 1B unique users and deliver 30B+ combined video plays and ad impressions every month. The Opportunity We are seeking a Staff Data Scientist to architect and drive the next generation of supply-side optimization systems. This is a highly hands-on, analytically rigorous role focused on model development, statistical reasoning, and a relentless curiosity for understanding why systems behave the way they do. You will lead the design of machine learning, statistical, and optimization models to maximize publisher yield (e.g., margin optimization, price-floor prediction, auction dynamics), grounded in deep exploratory data analysis and a causal understanding of marketplace behavior. You’ll help define and execute the roadmap for supply optimization systems operating at internet scale with measurable business impact. You will join a small, senior team of data scientists based in the US and Romania within the AI & Data Science group. This role will partner with the Chief AI Officer, Director of Data Science, and senior stakeholders to define roadmaps and prioritize initiatives with measurable revenue impact.

Requirements

Master’s or PhD in a quantitative discipline (Biostatistics, Physics, Statistics, Political Science, Economics, Applied Math, Psychology, or related).
10+ years of experience applying data science to complex, real-world systems.
Strong foundation in statistics, probability, and modeling under uncertainty.
Deep hands-on experience with machine learning models (e.g., gradient boosting, tree-based methods).
Experience evaluating models in environments where decisions affect future data.
Strong Python and SQL skills.
Familiarity with modern data and ML infrastructure (MLFlow, Airflow, Docker, Kubernetes, AWS).
Clear written and verbal communication skills.
Excellent communication skills across technical and executive audiences.
Valid passport (global company).

Responsibilities

Lead Advanced Exploratory Analysis
Conduct deep exploratory analysis to understand system dynamics, heterogeneity, and regime changes.
Identify structural drivers of performance degradation, including elasticity, interaction effects, and distribution shifts.
Translate analytical findings that diagnose what’s not working (and why), and translate to concrete modeling decisions.
Build and Advance Models
Design, build, and iterate on predictive and optimization models, including price floor prediction, bid landscape modeling, margin optimization, and inventory allocation.
Apply advanced machine learning techniques (e.g., gradient boosting, quantile regression, reinforcement learning) alongside classical statistical modeling where interpretability matters.
Evaluate models rigorously using sound statistical validation, offline analysis, and robustness checks.
Experimentation & Causal Inference
Define and oversee simulation frameworks, offline backtests, and online A/B experiments to measure model performance and business impact.
Apply causal inference techniques to isolate effects in noisy, non-stationary environments (seasonality, traffic shifts, publisher behavior changes.
Mentor & Influence
Provide technical leadership and mentorship to other data scientists.
Raise the bar on modeling quality, statistical rigor, experimentation design, and analytical communication.