m****g
发帖数: 40 | 1 保险公司属于比较难进的那种，因为我知道他们家年年招intern，但结果多数进不去。
给我offer算运气吧。我挺喜欢这家保险公司的，公司环境挺好，同事多是
statistician和精算师， 老美居多。
交待下本人背景，曾在银行做modeler，由于家庭原因relocate，所以工作时间也不长
。现在还是对银行的modeler工作情有独钟，但还没找到这方面的工作。我是MS，简历
上竞争不过PhD，面试机会都没有。其实，我感觉保险的模型更复杂点，业务也复杂。
也不知以后对回到银行做modeler有帮助吗？或者在保险做下去也很好？
版上能人很多，请指点一下。 |
S******y
发帖数: 1123 | 2 保险业的modeler从传统的精算为主，过渡到精算 + machine learning并行。估计每家
都在上马 Hadoop 或已经开始用Hadoop. 学会Hive 和Pig 应该可以得到加分
Good luck!
m*********[email protected]
http://plus.google.com/109275868505226513618/about
【在 m****g 的大作中提到】
: 保险公司属于比较难进的那种，因为我知道他们家年年招intern，但结果多数进不去。
: 给我offer算运气吧。我挺喜欢这家保险公司的，公司环境挺好，同事多是
: statistician和精算师， 老美居多。
: 交待下本人背景，曾在银行做modeler，由于家庭原因relocate，所以工作时间也不长
: 。现在还是对银行的modeler工作情有独钟，但还没找到这方面的工作。我是MS，简历
: 上竞争不过PhD，面试机会都没有。其实，我感觉保险的模型更复杂点，业务也复杂。
: 也不知以后对回到银行做modeler有帮助吗？或者在保险做下去也很好？
: 版上能人很多，请指点一下。
|
w********e
发帖数: 944 | |
h***i
发帖数: 3844 | 4 这个也是在扯蛋，都在用SAS。
【在 S******y 的大作中提到】
: 保险业的modeler从传统的精算为主，过渡到精算 + machine learning并行。估计每家
: 都在上马 Hadoop 或已经开始用Hadoop. 学会Hive 和Pig 应该可以得到加分
: Good luck!
: m*********[email protected]
: http://plus.google.com/109275868505226513618/about
|
c****t
发帖数: 19049 | 5 实在看不下去了。老兄你也太能胡扯了。靠点谱好吗？
【在 S******y 的大作中提到】
: 保险业的modeler从传统的精算为主，过渡到精算 + machine learning并行。估计每家
: 都在上马 Hadoop 或已经开始用Hadoop. 学会Hive 和Pig 应该可以得到加分
: Good luck!
: m*********[email protected]
: http://plus.google.com/109275868505226513618/about
|
A****t
发帖数: 141 | 6 这都看不出来，人家做广告呢
【在 c****t 的大作中提到】
: 实在看不下去了。老兄你也太能胡扯了。靠点谱好吗？
|
S******y
发帖数: 1123 | 7 哈哈 看来在保险公司做过的还真不多
我以前在的保险公司几年前已经在试安装调试Hadoop. 因为telematics device每三十
秒记录一次driving各方面的metrics 数据规模将来会非常大. 反正早晚都上Hadoop.
不如干脆早点上马.
当时没人会set up Hadoop,只有一家印度人开的consulting firm 号称自己是Hadoop专
家，当时就请了他们. 记得跟他们印度专家视频，印度那边是半夜两点多，两个老印哥
们不知从那儿抄了两段example code给我们做demo. 我当场差点笑翻了, 就是觉得那哥
俩半夜两点起来demo太可爱了 :-)
您说用SAS也沒错，SAS数据处理这块在保险公司人人都会。不会也不要紧，送你去做
SAS培训，保证你学会为止 :-)
当然SAS modeling 这块就靠个人统计功底了 :-)
m*********[email protected]
http://plus.google.com/109275868505226513618/about |
y*****w
发帖数: 1350 | 8 "当时没人会set up Hadoop", so your insurance company hired the Indian
consulting firm. Then "两个老印哥们不知从那儿抄了两段example code给我们做
demo. 我当场差点笑翻了" -- does this imply you were quite familiar with
Hadoop at that time so that you were well qualified in terms of the
technical skills to laugh at them? If you were, why did your insurance
company need to hire somebody else rather than directly asking you to do the
stuff?
【在 S******y 的大作中提到】
: 哈哈 看来在保险公司做过的还真不多
: 我以前在的保险公司几年前已经在试安装调试Hadoop. 因为telematics device每三十
: 秒记录一次driving各方面的metrics 数据规模将来会非常大. 反正早晚都上Hadoop.
: 不如干脆早点上马.
: 当时没人会set up Hadoop,只有一家印度人开的consulting firm 号称自己是Hadoop专
: 家，当时就请了他们. 记得跟他们印度专家视频，印度那边是半夜两点多，两个老印哥
: 们不知从那儿抄了两段example code给我们做demo. 我当场差点笑翻了, 就是觉得那哥
: 俩半夜两点起来demo太可爱了 :-)
: 您说用SAS也沒错，SAS数据处理这块在保险公司人人都会。不会也不要紧，送你去做
: SAS培训，保证你学会为止 :-)
|
l******n
发帖数: 9344 | 9 保险公司还hadoop呢，没用mainframe就算先进的啦。
【在 S******y 的大作中提到】
: 哈哈 看来在保险公司做过的还真不多
: 我以前在的保险公司几年前已经在试安装调试Hadoop. 因为telematics device每三十
: 秒记录一次driving各方面的metrics 数据规模将来会非常大. 反正早晚都上Hadoop.
: 不如干脆早点上马.
: 当时没人会set up Hadoop,只有一家印度人开的consulting firm 号称自己是Hadoop专
: 家，当时就请了他们. 记得跟他们印度专家视频，印度那边是半夜两点多，两个老印哥
: 们不知从那儿抄了两段example code给我们做demo. 我当场差点笑翻了, 就是觉得那哥
: 俩半夜两点起来demo太可爱了 :-)
: 您说用SAS也沒错，SAS数据处理这块在保险公司人人都会。不会也不要紧，送你去做
: SAS培训，保证你学会为止 :-)
|
S******y
发帖数: 1123 | 10 谢谢回复。
Hadoop is not rocket science. 概念其实很简单. 如今更是很普遍了。
保险业其实分几大块，health, life and P&C其实差別很大。基本上隔行如隔山。都说
insurance，可能具体理解不一定相同 我也只做过其中一块
听说AIG正在请人去做telematics方面的研究，以前的同事有跳槽去的。有兴趣的同学
可以去申请，应该算big data的经验
不好意思最近太忙了，后面的帖我如果没时间一一回复，提前抱歉了！
祝周末愉快！
【在 l******n 的大作中提到】
: 保险公司还hadoop呢，没用mainframe就算先进的啦。
|
|
|
s******t
发帖数: 70 | 11 你以前的公司是哪一家啊？
很感兴趣想了解一下他们telematics的情况
【在 S******y 的大作中提到】
: 谢谢回复。
: Hadoop is not rocket science. 概念其实很简单. 如今更是很普遍了。
: 保险业其实分几大块，health, life and P&C其实差別很大。基本上隔行如隔山。都说
: insurance，可能具体理解不一定相同 我也只做过其中一块
: 听说AIG正在请人去做telematics方面的研究，以前的同事有跳槽去的。有兴趣的同学
: 可以去申请，应该算big data的经验
: 不好意思最近太忙了，后面的帖我如果没时间一一回复，提前抱歉了！
: 祝周末愉快！
|
S******y
发帖数: 1123 | 12 已经pm您了
谢谢！
【在 s******t 的大作中提到】
: 你以前的公司是哪一家啊？
: 很感兴趣想了解一下他们telematics的情况
|
A*****r
发帖数: 795 | 13 完全错误
没见过这么乱扯的
做广告也得有点底线吧
【在 S******y 的大作中提到】
: 保险业的modeler从传统的精算为主，过渡到精算 + machine learning并行。估计每家
: 都在上马 Hadoop 或已经开始用Hadoop. 学会Hive 和Pig 应该可以得到加分
: Good luck!
: m*********[email protected]
: http://plus.google.com/109275868505226513618/about
|
S******y
发帖数: 1123 | 14 This is a very insightful article on insurance and big data, which talks
about big data initiatives taking place right now in insurance industry -
http://www.insurancenetworking.com/blogs/data-scientists-30443-
Do Insurers Really Need Data Scientists?
All across the land, there is a big-time push to bring in more “data
scientists” into companies, to help turn big data into big insights. But
who will be assuming these roles, and where will companies find them?
Recent research out of the McKinsey Global Institute finds there is an
impending shortage of key talent necessary for organizations to take
advantage of big data. Within the next few years, McKinsey predicts the
United States alone could face a shortage of 140,000 to 190,000 people with
deep analytical skills as well as 1.5 million managers and analysts with the
know-how to use the analysis of big data to make effective decisions.
McKinsey observes that the insurance and financial sectors are among the
sectors that are most likely to be putting big data to work.
Consider a recent ad from Allstate Insurance, in search of a “creative
predictive modeler/data scientist to use your machine learning, statistics,
data mining, and analytic skills to influence the decision making.” The
company sought a professional with a minimum of 3 to 5 years of experience
in a predictive modeling/data scientist role in the insurance industry or 4
to 7 years relevant experience outside of insurance.
Where do we find such people? In a new post, Ovum Research's Tony Baer says
they are difficult to find. “At last year’s Hadoop World, there was a
feeding frenzy for data scientists," he observes.
"Sometimes it seems like we’re looking for Albert Einstein or somebody
smarter."
That's because data science is “all about connecting the dots, not as easy
as it sounds,” he explains. “The V’s of big data—volume, variety,
velocity and value—require someone who discovers insights from data;
traditionally, that role was performed by the data miner. But data miners
dealt with better-bounded problems and well-bounded (and known) data sets
that made the problem more 2-dimensional. The variety of Big Data—in form
and in sources—introduces an element of the unknown. Deciphering Big Data
requires a mix of investigative savvy, communications skills, creativity/
artistry and the ability to think counter-intuitively. And don’t forget it
all comes atop a foundation of solid statistical and machine-learning
background plus technical knowledge of the tools and programming languages
of the trade.”
Again, where do we find such people? Tony points out that while some vendors
now offer training, there is a surprising lack of offerings of these skills
from systems integrators, and with data science talent scarce, we’d expect
that consulting firms would buy up talent that could then be “rented’ to
multiple clients. Excluding a few offshore firms, few SIs have yet stepped
up to the plate to roll out formal big data practices (the logical place
where data scientists would reside), but we expect that to change soon.
The laws of supply and demand will kick in for data scientists, but the ramp
up of supply won’t be as quick as that for the more platform-oriented data
architect or engineer. Of necessity, that supply of data scientists will
have to be augmented by software that automates the interpretation of
machine learning, but there’s only so far that you can program creativity
and counter-intuitive insight into a machine.
Joe McKendrick is an author, consultant, blogger and frequent INN
contributor specializing in information technology. |
d********5
发帖数: 31 | 15 谢谢楼主的文章，那data scientist的career path是什么呢，如果在保险行业的话
【在 S******y 的大作中提到】
: This is a very insightful article on insurance and big data, which talks
: about big data initiatives taking place right now in insurance industry -
: http://www.insurancenetworking.com/blogs/data-scientists-30443-
: Do Insurers Really Need Data Scientists?
: All across the land, there is a big-time push to bring in more “data
: scientists” into companies, to help turn big data into big insights. But
: who will be assuming these roles, and where will companies find them?
: Recent research out of the McKinsey Global Institute finds there is an
: impending shortage of key talent necessary for organizations to take
: advantage of big data. Within the next few years, McKinsey predicts the
|
