Engineering - Data
·
臺北
·
混合
Data Scientist
Responsibilities
- Collaborate with the business intelligence team to utilize historical data in forecasting future trends and detecting fraud.
- Collaborate with the operations team and the data engineering team to design various systems, which include, but are not limited to:
- User activity monitoring and anomaly detection
- Content moderation system
- Systems for optimization operation workflows
- Work with the data engineering team to design and implement recommendation systems at scale.
Requirements
- Fluency in common data science Python libraries, including but not limited to numpy, pandas, scikit-learn.
- Fluency in several machine learning frameworks, including but not limited to tensorflow / pytorch, keras, theanos, pyspark
- Proficiency in the basic software development lifecycle and low-level code optimization.
- A self-directed learning mentality.
- Comfortable working in an English-speaking environment.
Good to have
- Knowledge in deep learning based recommender system such as DLRM, Wide & Deep , and NCF.
- Expertise in building LLM applications and familiarity with foundational models (LLaMA, BERT, GPT, Mixtral 8x7B) and frameworks, such as langchain, weights & biases as well as retrieval mechanisms with vector databases such as pinecone and weaviate.
- Experience in leading a team and providing mentorship.
- 部門
- Engineering - Data
- 職位
- Data Scientist
- 地點
- 臺北
- 遠程狀態
- 混合
關於SWAG
SWAG 創立於 2016 年 8 月,最初的產品構想,是要打造一個能串連網紅創作者及用戶的 PGC 流量變現的平台,讓粉絲可以無時無刻的與心儀的網紅互動、讓網紅可以透過人氣實現獲利。
透過豐富多元的內容及強大的行銷推廣,目前平台有超過 400 萬註冊用戶及數千位內容提供者,用戶遍及臺灣、香港、澳門、新加坡、馬來西亞及歐美等,並獲得數十位 YouTuber 網紅大力推薦;成為亞洲最大的影音串流平台。
號
官方在 2018 上半年開啟了 swag.live ,使用者可以透過網頁直接開啟 SWAG,並陸續加入了直播、一對一私訊聊天以及影片解鎖等新功能。
如今 SWAG 定位為亞洲最大的影音串流社交平台,並且持續的拓展國際市場。
Engineering - Data
·
臺北
·
混合
Data Scientist
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