
Open since May 2026
Data Scientist, Analytics
AI Summary
Data Scientist in Appier's analytics team, driving experiments and building data pipelines for the Ad Cloud product to improve real-time ad delivery efficiency.
About this role
About Appier
Appier is a software-as-a-service (SaaS) 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.
About the role
We are looking for Data Scientists in Taiwan that will be part of our data science team. You will work closely with Machine Learning Scientists, Engineers, and other product related roles to perform data analytics for achieving Appier’s product and business goals.
Responsibilities
- Drive initiatives and experimentations in Ad Cloud product (e.g., enhance the efficiency to deliver ads to end users in real-time-bidding scenarios).
- Evaluate and define product and business metric framework.
- Build key data sets/pipelines to empower operational and exploratory analysis.
- Conduct data analysis reports to illustrate the results and insight (dashboards, reports)
About you
[Minimum qualifications]
- 5+ years of industry experience in the data analytics area.
- Can write efficient Python scripts for data analysis and job automation.
- Statistics knowledge and skills, using SQL to do basic data extraction and aggregation.
- Sufficient know-how of machine learning and basic experience of using machine learning tools.
- Fluent in Pandas or PySpark.
- Good analytical skills to extract meaningful insights from the data.
- Ability to communicate data analysis insight to audiences with different backgrounds.
- Proficient oral and written communication skills in English.
[Preferred qualifications]
- Working experiences in digital advertising industries.
#LI-RL1
Skills
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