在美國讀理工科很好找工作嗎？不盡如此
全美范圍內(nèi)，教育的重中之重可以被總結(jié)為由四個首字母構(gòu)成的縮略詞：STEM。這是情有可原的。精通科學(xué)(science)、技術(shù)(technology)、工程(engineering)和數(shù)學(xué)(mathematics)，對任何一個想要促進經(jīng)濟增長、推動科技創(chuàng)新、創(chuàng)造工作崗位的國家來說都至關(guān)重要。
The STEM campaign has been underway for years, championed by policymakers across the ideological spectrum, embraced in schools everywhere and by organizations ranging from the YWCA to the Boy Scouts. By now, the term — first popularized and promoted by the National Science Foundation — is used as a descriptive identifier. “She’s a STEM,” usually meant as a compliment, suggests someone who has a leg up in the college admissions sweepstakes.
STEM運動已開展多年，得到了秉持各種意識形態(tài)的政策制定者的支持，受到了各個地方的學(xué)校以及從基督教女青年會(YWCA)到男童子軍(Boy Scouts)等諸多組織的歡迎。目前，這個由國家科學(xué)基金會(National Science Foundation)率先提倡和推廣的概念，正被當成描述性識別符來使用。“她是一個STEM”，通常有恭維之意，指的是在大學(xué)入學(xué)資格大抽獎中具有優(yōu)勢的人。
Much of the public enthusiasm for STEM education rests on the assumption that these fields are rich in job opportunity. Some are, some aren’t. STEM is an expansive category, spanning many disciplines and occupations, from software engineers and data scientists to geologists, astronomers and physicists.
公眾對STEM教育的很大一部分興趣是基于這樣一種假設(shè)：這些領(lǐng)域有大把的工作機會。有些的確有，有些則不然。STEM是很寬泛的范疇，跨越很多學(xué)科和職業(yè)，從軟件工程師和數(shù)據(jù)科學(xué)家，到地質(zhì)學(xué)家、天文學(xué)家和物理學(xué)家。
What recent studies have made increasingly apparent is that the greatest number of high-paying STEM jobs are in the “T” (specifically, computing).
最近的研究越來越清楚地表明，高薪STEM工作崗位中，“T”類崗位數(shù)量最大(尤其是計算機類)。
Earlier this year, Glassdoor, a jobs listing website, ranked the median base salary of workers in their first five years of employment by undergraduate major. Computer science topped the list ($70,000), followed by electrical engineering ($68,438). Biochemistry ($46,406) and biotechnology ($48,442) were among the lowest paying majors in the study, which also confirmed that women are generally underrepresented in STEM majors.
今年早些時候，招聘網(wǎng)站Glassdoor按本科所學(xué)專業(yè)，對勞動者步入職場前五年的基本工資中位數(shù)做了排名。計算機科學(xué)位居榜單首位(70,000美元)，第二名是電子工程(68,438美元)。該研究所涉專業(yè)工資墊底的是生物技術(shù)(48,442美元)和生物化學(xué)(46,466美元);研究還表明，在STEM專業(yè)中，女性的比例總體而言是偏低的。
“There is a huge divide between the computing technology roles and the traditional sciences,” said Andrew Chamberlain, Glassdoor’s chief economist.
“計算機技術(shù)的地位與傳統(tǒng)科學(xué)大相徑庭，”Glassdoor的首席經(jīng)濟學(xué)家安德魯·張伯倫(Andrew Chamberlain)說。
At LinkedIn, researchers identified the skills most in demand. The top 10 last year were all computer skills, including expertise in cloud computing, data mining and statistical analysis, and writing smartphone applications.
領(lǐng)英(LinkedIn)的研究人員列出過需求量最大的技能。去年的前十名都是計算機類技能，其中包括云計算、數(shù)據(jù)挖掘、統(tǒng)計分析、編寫智能手機應(yīng)用程序等專門技能。
In a recent analysis, Edward Lazowska, a professor of computer science at the University of Washington, focused on the Bureau of Labor Statistics employment forecasts in STEM categories. In the decade ending in 2024, 73 percent of STEM job growth will be in computer occupations, but only 3 percent will be in the physical sciences and 3 percent in the life sciences.
在近期的分析中，華盛頓大學(xué)(University of Washington)的計算機科學(xué)教授愛德華·拉佐夫斯卡(Edward Lazowska)關(guān)注了美國勞工統(tǒng)計局(Bureau of Labor Statistics)對STEM領(lǐng)域的勞動用工預(yù)測。在截至2024年的10年間，新增的STEM工作崗位有73%會是計算機類職位，而物理科學(xué)和生命科學(xué)類職位分別只占3%。
A working grasp of the principles of science and math should be essential knowledge for all Americans, said Michael S. Teitelbaum, an expert on science education and policy. But he believes that STEM advocates, often executives and lobbyists for technology companies, do a disservice when they raise the alarm that America is facing a worrying shortfall of STEM workers, based on shortages in a relative handful of fast-growing fields like data analytics, artificial intelligence, cloud computing and computer security.
科學(xué)教育與政策專家邁克爾·S·泰特鮑姆(Michael S. Teitelbaum)表示，所有美國人都應(yīng)該掌握起碼的科學(xué)和數(shù)學(xué)原理。但他認為，一些STEM的擁護者——通常是科技公司的高管和說客——發(fā)出的警告是有害處的，他們說美國正面臨令人擔憂的STEM勞動者短缺問題，然而這種警告所依據(jù)的短缺出現(xiàn)在相對較少的幾個快速發(fā)展的領(lǐng)域內(nèi)，比如數(shù)據(jù)分析、人工智能、云計算和計算機安全等。
“When it gets generalized to all of STEM, it’s misleading,” said Mr. Teitelbaum, a senior research associate in the Labor and Worklife Program at Harvard Law School. “We’re misleading a lot of young people.”
“將其泛化至STEM涉及的所有領(lǐng)域，會造成誤導(dǎo)，”身為哈佛法學(xué)院(Harvard Law School)勞動和工作生活項目(Labor and Worklife Program)高級研究員的泰特鮑姆說。“我們在誤導(dǎo)大批年輕人。”
Unemployment rates for STEM majors may be low, but not all of those with undergraduate degrees end up in their field of study — only 13 percent in life sciences and 17 percent in physical sciences, according to a 2013 National Science Foundation survey. Computer science is the only STEM field where more than half of graduates are employed in their field.
STEM專業(yè)的失業(yè)率或許很低，但并非所有本科畢業(yè)生最終都能找到與專業(yè)對口的工作：美國國家科學(xué)基金會(National Science Foundation)2013年的一項調(diào)查顯示，生命科學(xué)畢業(yè)生只有13%找到了專業(yè)對口的工作，物理學(xué)只有17%。計算機科學(xué)是STEM領(lǐng)域唯一實現(xiàn)半數(shù)以上畢業(yè)生找到對口工作的學(xué)科。
If physicists and biologists want to enjoy the boom times in the digital economy, a few specialist start-ups will train them and find them jobs as data scientists and artificial intelligence programmers.
如果物理學(xué)家和生物學(xué)家想要在數(shù)字經(jīng)濟繁榮發(fā)展的時代分一杯羹，少數(shù)專家型初創(chuàng)公司會培訓(xùn)他們，為他們找到數(shù)據(jù)科學(xué)家、人工智能程序員之類的工作。
Insight Data Science Fellows Program, which has offices in New York, Boston, Seattle and Palo Alto, Calif., began its first training program five years ago and now has 900 alumni working at companies like Facebook, LinkedIn, Airbnb, Amazon and Microsoft. Jake Klamka, a physicist who founded the program, kept hearing from Silicon Valley executives that they had considered hiring traditional scientists, but converting them to technologists seemed time-consuming and risky. So Mr. Klamka decided he would start a company to provide scientists a smoother pathway into the tech industry.
洞見數(shù)據(jù)科學(xué)人才培養(yǎng)項目(Insight Data Science Fellows Program)在紐約、波士頓、西雅圖和加州帕洛阿托都有辦事處，于五年前開啟了其第一個培訓(xùn)項目，目前已有900名受訓(xùn)者進入Facebook、領(lǐng)英、Airbnb、亞馬遜(Amazon)和微軟(Microsoft)等公司工作。該項目創(chuàng)始人是物理學(xué)家杰克·克拉姆卡(Jake Klamka)，他之前總是聽硅谷的高管們說他們考慮過聘請傳統(tǒng)科學(xué)家，但把這些人變成技術(shù)專家似乎既耗時間又有風(fēng)險。于是克拉姆卡判定，他可以創(chuàng)辦一家公司，幫助科學(xué)家更順暢地進入科技行業(yè)。
Carlos Faham made that passage. He had an impressive academic career, with a string of grant awards and fellowships. His Ph.D. from Brown University was in dark-matter physics. After Brown, he was a postdoctoral fellow at the Lawrence Berkeley National Laboratory.
卡洛斯·法哈姆(Carlos Faham)走的就是這條路。他的學(xué)術(shù)生涯成就卓著，獲得過一系列助學(xué)獎和獎學(xué)金。他在布朗大學(xué)(Brown University)獲得的博士學(xué)位是暗物質(zhì)物理學(xué)方向。從那里畢業(yè)后，他曾在勞倫斯伯克利國家實驗室(Lawrence Berkeley National Laboratory)從事博士后研究。
Dr. Faham loved the research, but after nearly two years he was feeling the strain of that life. By then, he had spent 12 years in college, graduate school and postgraduate research. His next step would be to compete for a handful of tenure-track teaching openings across the country. For the pricey Bay Area, he wasn’t making enough. A postdoc researcher typically makes $40,000 to $60,000 a year.
法哈姆很喜歡在那里做研究，但過了近兩年后，他感受到了生活的壓力。那時他已經(jīng)在大學(xué)、研究生院和碩博研究領(lǐng)域度過了12年。他的下一步將是在全國各地爭取為數(shù)不多的終身教授職位。在消費水平很高的灣區(qū)，他賺的還不夠。一名博士后研究員通常每年掙4萬到6萬美元。
Dr. Faham had done serious programming for his physics research. He applied to tech companies, figuring they would be eager to hire someone with his intellectual firepower. He couldn’t get an in-person interview. He was told his background was too academic. He fumbled a couple of phone screening interviews because the statistical and machine-learning problems were unfamiliar to him.
法哈姆為自己的物理學(xué)研究做過認真的計劃。他申請過科技公司的一些職位，認為他們會渴望雇傭一個有他這種學(xué)術(shù)能力的人。但他得不到面試機會。他們說他的背景太學(xué)術(shù)化了。在幾次電話篩選面試中，他表現(xiàn)得很笨拙，因為他并不熟悉統(tǒng)計和機器學(xué)習(xí)方面的問題。
“It was like hitting a wall running at full speed, really humbling,” he recalled.
“感覺就像全速撞到一面墻上，真的很丟臉，”他回憶說。
Dr. Faham joined the seven-week Insight Data Science Fellows program in 2015. There was no formal course work. Other than a few tutorials by industry people, the time was spent creating a product — his was software for recognizing and tracking faces in video — and training for interviews. That involved solving a programming problem on a white board and explaining his thinking. “Interviewing is a muscle and you have to exercise it again and again,” he said. After the program, he received six job offers. He accepted the offer from LinkedIn. (Insight is free for participants; hiring companies pay an undisclosed fee.)
2015年，法哈姆參加了為期七周的洞見數(shù)據(jù)科學(xué)人才培養(yǎng)項目。該項目沒有正式的課程。除了業(yè)內(nèi)人士的一些指導(dǎo)課，剩下的時間都用來制作一個產(chǎn)品——他的產(chǎn)品是一個在視頻中識別和跟蹤人臉的軟件——以及面試培訓(xùn)。后者涉及在白板上解決一個編程問題，并解釋他的想法。“面試就像肌肉，必須反復(fù)練習(xí)，”他說。項目結(jié)束后，他得到了六個工作機會。他接受了LinkedIn的邀約(該項目不向參與者收費，招聘公司沒有透露自己支付的費用)。
Today, Dr. Faham, 33, is a senior data scientist, working on a team that uses machine learning and statistical models to detect illicit activity on the social network, including fake job listings, ad fraud, spam and bot attacks.
如今，33歲的法哈姆是一名高級數(shù)據(jù)科學(xué)家，他所在的團隊利用機器學(xué)習(xí)和統(tǒng)計模型來偵測社交網(wǎng)絡(luò)上的非法活動，包括虛假招聘機會、廣告欺詐、垃圾郵件和機器人攻擊。
The range of data-intensive detective work, he said, is “extremely rich” and “it moves so much faster than my previous world.” He makes a “pretty good six-figure salary,” about five times what he did as a postdoctoral researcher.
他說，數(shù)據(jù)密集型偵查工作的范圍“很廣”，“發(fā)展速度比我之前的世界快得多”。他現(xiàn)在的薪水“相當不錯，達到了六位數(shù)”，大約是他做博士后研究員時的五倍。
About 90 percent of those who enter the Insight program have landed jobs as data analysts, the company says, with a dropout rate of about 3 percent.
該公司表示，在參與洞見數(shù)據(jù)科學(xué)人才培養(yǎng)項目的人中，約有90%的人得到了做數(shù)據(jù)分析師的工作，退學(xué)率約為3%。
Anasuya Das made a similar career move, but not one as far from her academic training. After the program, Dr. Das, whose Ph.D. is in neuroscience, joined the Memorial Sloan Kettering Cancer Center in New York, where she is now a senior data scientist. She works on a team that creates software tools for the center’s doctors, nurses and researchers. One current project is a program to recommend the most promising clinical trials for individual cancer patients, based on their medical histories, age, gender and genetics.
阿納蘇亞·達斯(Anasuya Das)也做了類似的職業(yè)轉(zhuǎn)變，但并沒有過多偏離自己的專業(yè)背景。項目結(jié)束后，擁有神經(jīng)科學(xué)博士學(xué)位的達斯加入了紐約的紀念斯隆·凱特林癌癥中心(Memorial Sloan Kettering Cancer Center)，現(xiàn)在她是那里的高級數(shù)據(jù)科學(xué)家。她所在的團隊為該中心的醫(yī)生、護士和研究人員創(chuàng)建軟件工具。目前的一個項目是根據(jù)每位癌癥患者的病史、年齡、性別和基因，推薦最可能有效的臨床試驗。
Data science is distinctly different from neuroscience, Dr. Das said, but some of the tools she employs, like a machine-learning technique called artificial neural networks, do take their inspiration from the brain. Her experience points to the larger trend that digital technologies like data science and artificial intelligence are increasingly being used in nearly every discipline. So technology and the other STEM fields merge.
達斯說，數(shù)據(jù)科學(xué)與神經(jīng)科學(xué)截然不同，但她使用的某些工具，比如一種被稱為人工神經(jīng)網(wǎng)絡(luò)的機器學(xué)習(xí)技術(shù)，確實是受到大腦的啟發(fā)。她的經(jīng)歷反映出一個更大的趨勢：數(shù)據(jù)科學(xué)和人工智能等數(shù)字技術(shù)正越來越多地被應(yīng)用于幾乎所有學(xué)科。也就是說，技術(shù)和STEM的其他領(lǐng)域融合在了一起。
That is the thinking behind a new division of data sciences at the University of California, Berkeley, that started in July. The division is a response to student demand and advancing technology. Berkeley’s “Foundations of Data Science” course attracted 1,200 students from more than 50 majors in the last academic year.
加州大學(xué)伯克利分校(University of California, Berkeley)正是出于這方面的考慮，創(chuàng)設(shè)了全新的數(shù)據(jù)科學(xué)部。它是為回應(yīng)學(xué)生需求和技術(shù)進步而設(shè)立的。伯克利分校的“數(shù)據(jù)科學(xué)基礎(chǔ)”課程在上個學(xué)年吸引了50多個專業(yè)的1200名學(xué)生。
The choice of the term “division” rather than “institute,” explained David Culler, the interim dean for data sciences, underlines its approach. “We want this to be something foundational across the university, innovating with other disciplines, not differentiating from them,” he said. “This is the academic world mirroring what is happening in the larger economy.”
暫任數(shù)據(jù)科學(xué)部主任的戴維·考勒(David Culler)解釋說，稱它為“部”(division)而非“學(xué)院”是為了強調(diào)它的思維。“我們希望它是整個大學(xué)的基礎(chǔ)性部門，與其他學(xué)科共同創(chuàng)新，而不是與它們區(qū)別開來，”他說，“這是整體經(jīng)濟動向在學(xué)術(shù)界的反映。”
