Data Scientist

Samsung Ads is an advanced advertising technology company in rapid growth that focuses on enabling brands to connect with Samsung TV audiences as they are exposed to digital media by using the industry’s most comprehensive data to build the world’s smartest advertising platform. Being part of an international company such as Samsung and doing business around the world means that we get to work on the most challenging projects with stakeholders and teams located around the globe.

We are proud to have built a world-class organization grounded in an entrepreneurial and collaborative spirit. Working at Samsung Ads offers one of the best environments in the industry to learn just how fast you can grow, how much you can achieve, and how good you can be. We thrive on problem‑solving, breaking new ground, and enjoying every part of the journey.

We are seeking an experienced Senior Staff Data Scientist to lead our experimentation ad measurement initiatives at Samsung Ads Data Science team. This is a high‑impact technical leadership role where you'll shape how we measure success, optimize our advertising platform, and drive data‑informed decision‑making across the organization. You'll work at the intersection of advanced analytics, product strategy, and engineering to build robust frameworks that power our advertising ecosystem.

Location:

Mountain View, CA

• Serve as the technical lead for experimentation and measurement initiatives within the data science team of Samsung Ads.
• Collaborate closely with Product Managers, ML Engineers, and cross‑functional stakeholders to define and execute high‑impact projects from conception to deployment.
• Provide mentorship and technical guidance to data scientists on the team.

• Scale and enhance our ML A/B testing framework to support sophisticated experimental designs and rapid iteration.
• Build comprehensive metrics and measurement frameworks to evaluate machine learning model performance and business impact.
• Deploy standardized revenue potential assessment models and sizing methodologies to guide product investment decisions.

• Design and implement comprehensive measurement systems that track operational, product, and ML contributions across our entire advertising platform.
• Develop sophisticated causal inference models and multi‑touch attribution frameworks to understand true business drivers.
• Create systematic processes and automation pipelines that convert analytical insights into concrete business actions with clear accountability.

• Master’s degree with 8+ years of industry experience, OR PhD with 6+ years of industry experience.
• Degree in Mathematics, Statistics, Economics, Computer Science, or related quantitative field.
• Deep understanding and hands‑on experience with advanced experimentation methodologies and A/B testing frameworks, causal inference techniques and statistical modeling, attribution modeling and multi‑touch attribution systems, machine learning evaluation and measurement frameworks.
• Fluent in SQL and Python for data analysis, modeling, and automation.
• Experience with large‑scale data systems and cloud platforms.
• Excellent communication skills with proven ability to influence stakeholders and drive cross‑team decisions.
• Experience mentoring and leading technical teams.

• Prior experience in advertising industry and real‑time bidding (RTB) ecosystem.
• Background in econometrics or marketing mix modeling.
• Hands‑on experience with production‑grade machine learning solutions and/or software development.

Compensation for this role, for candidates based in New York, NY, is expected to be between $240,000 ~ $280,000. Actual pay will be determined considering factors such as relevant skills and experience, and comparison to other employees in the role.

Regular full‑time employees (salaried or hourly) have access to benefits including Medical, Dental, Vision, Life Insurance, 401(k), Employee Purchase Program, Tuition Assistance (after 6 months), Paid Time Off, Student Loan Program (after 6 months), Wellness Incentives, and many more.