From pioneering affiliate marketing to powering the future of connected commerce, CJ has been the trusted leader in partnership-based growth since 1998.

Founded in Santa Barbara, California, CJ (formerly Commission Junction) is the world’s most established performance marketing platform, trusted by over 3,800 global brands across retail, travel, finance, and home services. Our technology and expertise connect 167K+ publishers and brands to billions of consumers worldwide, driving measurable, profitable growth.

As part of Publicis Groupe, and aligned with Publicis Media, we unite data, technology, and strategic expertise to deliver a truly customer-centric approach. With a legacy of innovation and a vision for what’s next, CJ continues to set the standard for performance marketing — building partnerships that scale, campaigns that perform, and commerce that connects.

Overview:

In this client-facing Data Scientist role at CJ, you will serve as a dedicated analytics partner to a large enterprise client, delivering data-driven insights and advanced analytical solutions in a digital advertising environment. You will own end-to-end projects that support campaign performance, measurement, and optimization, helping drive growth through high-quality data, clear reporting, and actionable recommendations.

You will work closely with client stakeholders, account teams, and cross-functional partners to translate business needs into scalable data solutions. This role is ideal for someone who thrives in a fast-paced, client-facing environment and enjoys combining technical expertise with strategic problem-solving to influence marketing outcomes.

Responsibilities:

What you will do:

· Lead and support advanced client-facing data science and analytics projects, including forecasting, incrementality measurement, and program optimization.

· Act as a strategic partner to account teams, proactively identifying opportunities, shaping measurement strategies, and influencing decision-making.

· Apply hypothesis-driven analysis to diagnose performance and quantify impact.

· Take ownership of project scoping, data querying and modeling, quality checks, and delivery of actionable insights.

· Analyze large-scale e-commerce and advertising datasets, leveraging statistical and ML methods to uncover opportunities and support growth strategies.

· Design and interpret incrementality tests, marketing mix models (MMM), and lift tests to assess causal impact and optimize marketing spend.

· Develop machine learning models, including forecasting and inferential analysis, and implement LLM-driven solutions where applicable.

· Create and deliver Tableau dashboards and reporting that distill complex analysis into clear, actionable insights for both internal and external stakeholders.

· Collaborate with account teams, engineers, and other data scientists to design and implement scalable data solutions.

· Present technical findings and recommendations to non-technical audiences, demonstrating strong client-facing and communication skills.

· Present complex analytical concepts in intuitive ways tailored to varying levels of stakeholder sophistication.

What we look for:

· 3–5 years of professional experience in Data Science and Analytics.

· 2+ years of hands-on experience with PySpark, Scala, and Tableau.

· Must have Ad Tech / MarTech industry experience, specifically in e-commerce, travel, and finance.

· Strong client-facing experience, including presentations and solution delivery.

· Proven ability to deliver scalable data solutions and actionable insights in digital advertising or affiliate marketing.

· Proficiency with GitLab or GitHub for version control and collaborative development.

· Experience developing alongside LLM code assistants (e.g., Copilot, Claude, Codex).

· Preferred: Experience implementing LLMs into business workflows, including training, fine-tuning, and manipulation for applied use cases.

· Experience with incrementality testing, MMM, and lift test design and analysis.

· Strong communication skills with the ability to simplify and explain complex technical results to non-technical stakeholders.