Senior Machine Learning Scientist (Ads incrementality) - Tokyo

appier· Scientist
Apply Now ↗
📍 Tokyo, Japan

About this role

About Appier 

Appier is an AI-native Agentic AI as a Service (AaaS) company that uses artificial intelligence (AI) to power business decision-making. Founded in 2012 with a vision of democratizing AI, Appier’s mission is turning AI into ROI by making software intelligent. Appier now has 17 offices across APAC, Europe and U.S., and is listed on the Tokyo Stock Exchange (Ticker number: 4180). Visit www.appier.com for more information.

Open to overseas candidates/Visa Support

This position is based in Tokyo, Japan. For international candidates, Appier's Japan office provides visa sponsorship to ensure a smooth transition to Japan.

The Impact You’ll Make at Appier

Appier is seeking a Senior Machine Learning Scientist to join our Advertising Cloud Optimization team, which leads the development of core machine learning algorithms driving campaign efficiency and advertiser ROI. Our programmatic advertising platform operates at a massive scale, handling over multi millions queries per second (QPS), all powered by our proprietary deep learning models for bidding, pricing, and personalized content delivery.
In this role, you'll measure ads incrementality on different type of traffic, creative, users, and also improve campaign efficiency on combination having good incrementality by machine learning model and AI automation.

What You’ll Work On

 

  • Leverage scientific methods to improve ads measurement.
  • Drive online experiments to continuously improve ads effectiveness.
  • Own the project independently.
  • Partner with product stakeholder, backend and frontend to provide measurement solution for our customers.

 

What We’re Looking For

 

  • Master’s or PhD’s degree in Computer Science, Machine Learning, Statistics, Econometrics, or related field.
  • 3+ years of industry experience on causal inference, incrementality, marketing mix modeling or other measurement solutions on digital Ads related fields, and understand the assumption and difference across methods.
  • Proficiency in Python and SQL experience with modern ML frameworks (PyTorch, TensorFlow, etc.).
  • Strong ownership and collaboration skills.

 

Language

Fluent in English or Chinese.

#LI-AK1

 

Frequently Asked Questions

Is the salary disclosed for the Senior Machine Learning Scientist (Ads incrementality) - Tokyo position at appier?
The salary for this Senior Machine Learning Scientist (Ads incrementality) - Tokyo role at appier is not publicly listed. Click "Apply Now" to learn more about the compensation package on their official careers page.
Where is the Senior Machine Learning Scientist (Ads incrementality) - Tokyo position at appier located?
This Senior Machine Learning Scientist (Ads incrementality) - Tokyo role at appier is based in Tokyo, Japan. The position is listed as on-site or hybrid. Check the full job description or apply directly to confirm the work arrangement.
Which team or department does the Senior Machine Learning Scientist (Ads incrementality) - Tokyo at appier belong to?
This Senior Machine Learning Scientist (Ads incrementality) - Tokyo position is part of the Scientist department at appier. See the full job description for more information about the team structure and responsibilities.
How do I apply for the Senior Machine Learning Scientist (Ads incrementality) - Tokyo position at appier?
Click the "Apply Now" button on this page. You will be redirected to appier's official application portal hosted on greenhouse where you can submit your application directly.
When was the Senior Machine Learning Scientist (Ads incrementality) - Tokyo job at appier posted?
This Senior Machine Learning Scientist (Ads incrementality) - Tokyo position at appier was posted on Oct 30, 2025. Apply as soon as possible — early applications are often reviewed first.
Senior Machine Learning Scientist (Ads incrementality) - Tokyo
appier
Apply for this role ↗

You'll be redirected to appier's official application page on Greenhouse.