{"id":135375,"date":"2020-07-12T21:48:01","date_gmt":"2020-07-12T13:48:01","guid":{"rendered":"http:\/\/4563.org\/?p=135375"},"modified":"2020-07-12T21:48:01","modified_gmt":"2020-07-12T13:48:01","slug":"nlp-%e5%be%8b%e5%95%86%e8%81%94%e8%ae%af%ef%bc%8c%e4%b8%8a%e6%b5%b7%e5%a4%96%e4%bc%81%ef%bc%8c-wlb%ef%bc%8c%e8%87%aa%e6%9c%89%e6%95%b0%e6%8d%ae%e5%ba%93%e6%90%9c%e7%b4%a2%e5%bc%95%e6%93%8e%e6%9c%8d","status":"publish","type":"post","link":"http:\/\/4563.org\/?p=135375","title":{"rendered":"[NLP] \u5f8b\u5546\u8054\u8baf\uff0c\u4e0a\u6d77\u5916\u4f01\uff0c WLB\uff0c\u81ea\u6709\u6570\u636e\u5e93\u641c\u7d22\u5f15\u64ce\u670d\u52a1"},"content":{"rendered":"
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[NLP] \u5f8b\u5546\u8054\u8baf\uff0c\u4e0a\u6d77\u5916\u4f01\uff0c WLB\uff0c\u81ea\u6709\u6570\u636e\u5e93\u641c\u7d22\u5f15\u64ce\u670d\u52a1 <\/h1>\n

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\u8cc7\u6df1\u5927\u4f6c : matthewye0724 <\/span> <\/i> 35<\/span> <\/div>\n
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\u5b98\u7f51\uff1alexiscn.com
\u4ecb\u7ecd\uff1a
https:\/\/mp.weixin.qq.com\/s?__biz=MjM5NTkyNjM0Mw==&mid=2651242762&idx=2&sn=2a65cd3018779f98cbe911a5b5ea99ec&chksm=bd0379228a74f0349f75554ec980d403bf50e4a9e743bbdde474c16c4facaa1a12bb51b226c3&mpshare=1&scene=1&srcid=0422TG5zlt5GUL4Yxi1F4yvY&sharer_sharetime=1587527756299&sharer_shareid=43676ef3a54955f21433a504958f8c3d#rd<\/p>\n

\u5f8b\u5546\u8054\u8baf\u6210\u7acb\u4e8e:1818 \u5e74
\u5168\u7403\u603b\u90e8\u6240\u5728\u5730:\u7ebd\u7ea6
\u6bcd\u516c\u53f8:\u52b1\u8baf\u96c6\u56e2
\u5168\u7403\u5458\u5de5\u6570\u91cf:\u8d85\u8fc7 10,000 \u540d
\u5168\u7403\u4e1a\u52a1:\u670d\u52a1\u4e8e\u5168\u7403 175 \u4e2a\u56fd\u5bb6\u7684\u5ba2\u6237<\/p>\n

NLP
JOB RESPONSIBILITIES
\u00a0
\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Perform hands-on data analysis and modelling with huge data sets.
\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Apply data mining, NLP, and machine learning\/deep learning (both supervised and unsupervised) to improve relevance and personalization algorithms.
\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Work side-by-side with product managers, software engineers, and designers in designing experiments and minimum viable products.
\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Discover data sources, get access to them, import them, clean them up, and make them \u201cmodel-ready\u201d. You need to be willing and able to do your own ETL.
\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Create and refine features from the underlying data. You\u2019ll enjoy developing just enough subject matter expertise to have an intuition about what features might make your model perform better, and then you\u2019ll lather, rinse and repeat.
\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Run regular A\/B tests, gather data, perform statistical analysis, draw conclusions on the impact of your optimizations and communicate results to peers and leaders.
\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Explore new design or technology shifts to determine how they might connect with the customer benefits we wish to deliver.
\u00a0
REQUIREMENTS
\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 BS, MS, or PhD in an appropriate technology field (Computer Science, Statistics, Applied Math, Operations Research, etc.).
\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 2+ year\u2019s experience with data science & NLP.
\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Strong programming skills with Python.
\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Efficient in SQL, Hive, or Spark SQL, etc.
\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Comfortable in Linux environment
\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Experience in data mining algorithms and statistical modelling techniques such as clustering, classification, regression, decision trees, neural nets, support vector machines, anomaly detection, recommender systems, sequential pattern discovery, and text mining.
\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Familiar with latest development in NLP & deep learning
\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Solid communication skills: Demonstrated ability to explain complex technical issues to both technical and non-technical audiences.<\/p>\n

\u6b22\u8fce\u53d1\u9001 \u7b80\u5386 \u5230 [email protected] <\/p><\/div>\n

\u5927\u4f6c\u6709\u8a71\u8aaa<\/b> (0<\/span>) <\/div>\n
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    [NLP] \u5f8b\u5546\u8054\u8baf\uff0c\u4e0a\u6d77\u5916\u4f01\uff0c …<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[],"tags":[],"_links":{"self":[{"href":"http:\/\/4563.org\/index.php?rest_route=\/wp\/v2\/posts\/135375"}],"collection":[{"href":"http:\/\/4563.org\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/4563.org\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/4563.org\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"http:\/\/4563.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=135375"}],"version-history":[{"count":0,"href":"http:\/\/4563.org\/index.php?rest_route=\/wp\/v2\/posts\/135375\/revisions"}],"wp:attachment":[{"href":"http:\/\/4563.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=135375"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/4563.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=135375"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/4563.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=135375"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}