a*********n
发帖数: 44 | 1 很惶恐，不知道会怎么准备，是像传统码农那样算法 + 扩展到Big Data，还是要推倒
个Convex Optimization啥的，或者其他？
拜谢！ | v*****k
发帖数: 7798 | 2 这个不同公司区别很大
【在 a*********n 的大作中提到】
: 很惶恐，不知道会怎么准备，是像传统码农那样算法 + 扩展到Big Data，还是要推倒
: 个Convex Optimization啥的，或者其他？
: 拜谢！
| a*********n
发帖数: 44 | 3 比如Facebook呢？
【在 v*****k 的大作中提到】
: 这个不同公司区别很大
| c***z
发帖数: 6348 | | a*********n
发帖数: 44 | 5 记得之前版上有前辈报过offer，12万+18万股票+3万签字费
Responsibilities
Work closely with a product engineering team to identify and answer
important product questions
Answer product questions by using appropriate statistical techniques on
available data
Communicate findings to product managers and engineers
Drive the collection of new data and the refinement of existing data sources
Analyze and interpret the results of product experiments
Develop best practices for instrumentation and experimentation and
communicate those to product engineering teams
Requirements
M.S. or Ph.D. in a relevant technical field, or 4+ years experience in a
relevant role
Extensive experience solving analytical problems using quantitative
approaches
Comfort manipulating and analyzing complex, high-volume, high-dimensionality
data from varying sources
A strong passion for empirical research and for answering hard questions
with data
A flexible analytic approach that allows for results at varying levels of
precision
Ability to communicate complex quantitative analysis in a clear, precise,
and actionable manner
Fluency with at least one scripting language such as Python or PHP
Familiarity with relational databases and SQL
Expert knowledge of an analysis tool such as R, Matlab, or SAS
Experience working with large data sets, experience working with distributed
computing tools a plus (Map/Reduce, Hadoop, Hive, etc.)
【在 c***z 的大作中提到】
: 上job description吧，不然没法说
| j*****n
发帖数: 1545 | 6 fresh PhD?
sources
【在 a*********n 的大作中提到】
: 记得之前版上有前辈报过offer，12万+18万股票+3万签字费
: Responsibilities
: Work closely with a product engineering team to identify and answer
: important product questions
: Answer product questions by using appropriate statistical techniques on
: available data
: Communicate findings to product managers and engineers
: Drive the collection of new data and the refinement of existing data sources
: Analyze and interpret the results of product experiments
: Develop best practices for instrumentation and experimentation and
|
|
