Tiktok Monetization Product Data Scientist

6 days ago

Singapore TikTok Full time

TikTok is the leading destination for short-form mobile video. Our mission is to inspire creativity and bring joy. TikTok has global offices including Los Angeles, New York, London, Paris, Berlin, Dubai, Singapore, Jakarta, Seoul and Tokyo.

**About the TikTok Monetization Product Data Science team**:
The TikTok Monetization Product Data Science team plays a critical role in driving initiatives to ensure efficient and healthy monetization through Ads and Commerce ecosystems.
You will work on an exciting array of newly launched or in-the-pipeline advertising platforms such as Livestream and E-Commerce.

**Responsibilities**:
1. Develop end to end analysis framework and distill replicable methodology to evaluate and improve Ads & E-Commerce ecosystem to ensure healthy as well as maximal monetization;
2. Liaise with multiple stakeholders across inter-functional teams to get buy-in and land above framework and co-create actionable, divisible improvement plans;
3. Build and maintain pipelines and visualization to monitor results of the improvement plans and identify exploitable opportunity areas;
4. Train machine learning models to classify, score and flag creatives or advertisers potentially detrimental to user experience and platform integrity;
5. Work closely with cross-functional partners (ad policy, product management, data analytics, operations, engineering) to identify, assess and propose solutions to escalations from users, advertisers, creators etc.
1. Bachelor's degree or above with a background in computer science, data science, analytics, math or statistics;
2. 2+ years' experience in a matrix environment with cross-regional collaborations;
3. Strong SQL capability (CTE, aggregate function etc.);
4. Strong analytical and research skills;
5. Proficiency in English is required; any second language proficiency is highly preferred but not required;
6. Proficiency with any scripting language, e.g. Python;
7. Proficiency with machine learning packages such sklearn, pyspark etc. to construct end to end GLM models with hyperparameter tuning.

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