IT职业路线图：数据科学家

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数据科学涉及使用科学方法、算法和系统从结构化和非结构化数据中提取见解。作为一门学科，数据科学综合了数学、统计学、计算机科学、领域知识和其他输入来分析事件和趋势。

在一个数字化的世界里，数据科学家是最受欢迎的IT专业人士之一。从根本上说，数据科学家应该能够编写干净的代码，并使用统计数据从数据中获得见解。

根据职业网站Indeed.com的说法，数据科学家不仅要结合数学和计算机科学，还必须了解他们所服务的行业。数据科学家使用非结构化数据来生成与其领域相关的报告和解决方案。

Indeed认为，数据科学家应该熟悉云计算、统计学、高等数学、机器学习、数据可视化工具、查询语言和数据库管理。通常期望能够使用Python和R编程。

数据科学家薪资

职业网站Glassdoor表示，在美国，数据科学家的总薪酬估计为每年120660美元，平均年薪为99672美元。

人力资源公司Robert Half指出，在数据科学领域找到工作，尤其是在入门级，并不是不可逾越的。尽管最近裁员，但科技行业的招聘仍很活跃，因为IT雇主的招聘人数达到或超过了疫情前的水平。

Robert Half表示:“随着企业加速数字化转型，所有主要业务部门(从技术和制造业到金融服务和医疗保健)以及学术界、政府和非营利部门的组织都需要数据科学家。这是因为所有类型的组织都需要将数字转化为建议的策略和行动。”

为了了解成为一名数据科学家需要做些什么，我们采访了出行即服务提供商优步科技公司的数据科学家达里尔·康。

一、教育

Daryl Kang，Uber科技公司数据科学家

Kang获得了加州大学洛杉矶分校的文学学士学位，在那里他主修商业经济学，辅修会计。他说：“我是第一代大学生，我用了两年半的时间以最优等的成绩毕业，这让我有足够的资金去读研究生。”

Kang继续在哥伦比亚大学攻读数据科学硕士学位。申请数据科学课程需要具备数学、概率、统计学和计算机科学的基础。

Kang说。“我最初的动机是在银行和金融领域发展，我毕业于经济学专业，本以为这是最自然的职业道路。”

然而，在大学毕业后的间隔年里，Kang有机会从事与他的热情相符的个人项目。他说:“受《魔鬼经济学》这本书的启发，我决定主修经济学。它向我展示了数据在回答任何领域都普遍适用的问题方面的力量。”

大约在这个时候，Kang也发现了对编程的热情，他说，“在Excel中，他遇到了可能的天花板。”他花了几个月的时间通过免费的在线课程学习如何编程。

了解积极挑战和消极挑战之间的区别是很重要的。放弃错误的追求能让我们专注于重要的事情。

Kang说：“这让我走上了一条明确的道路，最终发现了数据科学领域，并清楚地认识到它是我对经济学热情的延续，在这一点上，我决定继续攻读数据科学的研究生课程，实现职业转变。”

二、基础:纪律、激情和同理心

在马来西亚长大的Kang说，他经历了严格的公共教育体系，“纪律是灌输给我的关键价值观。这无疑为我建立强大的职业道德奠定了基础，这对我的数据科学职业生涯有帮助，因为这个角色可能要求很高。”

此外，Kang在加州大学洛杉矶分校文科项目的经历培养了他对其他研究领域的欣赏意识，以及对学习的普遍渴望。他说:“这给了我纪律，但更重要的是激情，让我追求持续学习，这对跟上数据科学领域的发展至关重要。”

Kang还指出，从非技术背景开始帮助他与非技术利益相关者产生共鸣，这有助于他在自己的角色中有效地沟通。

三、工作经历

Kang第一次接触数据科学工作是在娱乐公司Viacom（现为Paramount）的实习期间。他做了7个月的数据科学家实习生。他说:“这是我在业内第一次真正接触数据科学。我的工作是预测票房收入。”

这段经历帮助Kang有为弥合了学术界和产业界之间的差距。他说，为了在应用数据科学领域取得成功，他能够发现自己技能组合中的差距。

2018年，Kang以数据科学家的身份加入媒体公司Forbes，主要专注于构建推荐系统。一个例子是一个向新闻编辑室的作者推荐热门新闻文章的系统。

Kang说：“公司非常重视后端工程，这给了我一个更好地提高软件工程技能的机会，这也是一个体验交付数据产品的端到端生命周期的机会，从设置后端基础设施，到分析数据中的见解，再到将这些见解呈现给最终用户。”

为了有效地履行他在Forbes的职责，Kang需要在Python和软件架构方面有坚实的基础。

在Uber工作了大约三年后，Kang加入Uber，担任数据科学家，主要负责产品分析。“我专门负责商户增长和收购。这意味着可交付成果更侧重于告知业务决策和提供产品建议。”Kang指出，数据工程也是该角色的重要组成部分。“必须整合来自多个来源的数据，以正确地传达业务状态。”

在Uber，Kang说他必须精通实验设计，“这是Uber制定数据驱动决策原则的核心部分。”（华东CIO大会、华东CIO联盟、CDLC中国数字化灯塔大会、CXO数字化研学之旅、数字化江湖-讲武堂，数字化江湖-大侠传、数字化江湖-论剑、CXO系列管理论坛（陆家嘴CXO管理论坛、宁波东钱湖CXO管理论坛等）、数字化转型网，走进灯塔工厂系列、ECIO大会等）

四、数据科学家典型的工作周

Kang说:“毫无疑问，会议是一周的重要组成部分。这些都是提供报告、演示和为利益相关者建立同理心的机会。”这些利益相关者通常是产品经理，但与用户体验研究人员、产品设计师或工程师等其他工作职能部门合作也不罕见。

“取决于手头的项目，剩下的时间可以用来做分析——例如，运行描述性分析来准备月度绩效报告，或诊断性分析来调查指标的变化——制作演示文稿，或者更具体地定义叙述并得出建议，”Kang说。

五、难忘的职业时刻

Kang说:“我在Forbes工作期间最美好的回忆之一是指导一个研究生团队完成他们的顶点项目，这是一个行业推广计划的一部分。“第一次扮演导师的角色让人耳目一新，对我和学生来说都是一次学习经历。我们的团队还在期末展示比赛中获得了第一名，这简直是锦上添花。”

六、职业建议

Kang说：“命运青睐勇敢者，许多事情一开始似乎无法克服，但随着时间的推移和重复会变得容易。此外，了解积极挑战和消极挑战之间的区别也很重要。放弃错误的追求能让我们专注于重要的事情。”

实际上，Kang建议任何对数据科学感兴趣的人都应该从学习Python和统计学开始。“如果你没有被吓倒，并且有足够的好奇心，接下来你自然会进入数据科学和机器学习领域。”

原文：

Data science involves using scientific methods, algorithms, and systems to extract insights from structured and unstructured data. As a discipline, data science synthesizes mathematics, statistics, computer science, domain knowledge, and other inputs to analyze events and trends.

In a world gone digital, data scientists are among the most highly sought IT professionals. Fundamentally, a data scientist should be able to write clean code and use statistics to derive insights from data.

According to the career site Indeed.com, data scientists not only combine mathematics and computer science but must understand the industry they serve. Data scientists use unstructured data to produce reports and solutions related to their field.

According to Indeed, data scientists should be familiar with cloud computing, statistics, advanced mathematics, machine learning, data visualization tools, query languages, and database management. The ability to program with Python and R is generally expected.

Data scientist salary

Career site Glassdoor says the estimated total pay for a data scientist is $120,660 per year in the United States, with an average annual salary of $99,672.

The staffing firm Robert Half notes that landing jobs in data science, particularly at the entry level, is not insurmountable. Despite recent cutbacks, recruiting for the technology sector remains active, as IT employers are hiring at or beyond pre-pandemic levels.

“As businesses accelerate their digital transformation, data scientists are needed across all major business sectors—from technology and manufacturing to financial services and healthcare—as well as organizations in academia, government, and the nonprofit sector,” says Robert Half. “That’s because organizations of all types need to turn numbers into recommended strategies and actions.”

To find out what’s involved in becoming a data scientist, we spoke with Daryl Kang, data scientist at mobility-as-a-service provider Uber Technologies.

Education

Kang earned a Bachelor of Arts degree from the University of California, Los Angeles, where he majored in business economics with a minor in accounting. “I was a first-generation college student,” he says. “I graduated summa cum laude in 2.5 years, which allowed me the financial wherewithal to pursue graduate school.”

Kang went on to pursue a Master of Science degree in data science at Columbia University. Qualifying for the data science program required a foundation in math, probability, statistics, and computer science.

“I was originally motivated to pursue a career in banking and finance,” Kang says. “Having graduated with a degree in economics, I had assumed this to be the most natural career path.”

However, during a gap year after finishing college, Kang had the opportunity to work on personal projects that aligned with his passions. “I was motivated to major in economics after being inspired by the book, Freakonomics," he says. “It showed me the power of data in answering questions that were universally applicable to any field.”

Around this time, Kang also discovered a passion for programming, after “running into the ceiling of what was possible with Excel,” he says. He devoted several months to learning how to program through free online courses.

It's important to know the difference between a positive and negative challenge. Quitting the wrong pursuits enables us to focus on the things that matter.

“This set me on a clear path to eventually discovering the field of data science, and with it the clarity of recognizing it as a continuation of my passion for economics,” Kang says. “At this point, I was determined to pursue my graduate studies in data science to make the career switch.”

Foundations: Discipline, passion, and empathy

Growing up in Malaysia, Kang says he experienced a strict public education system, “where discipline was a key value that was instilled in me. This definitely set the stage for building a strong work ethic that helped in my data science career, since the role can be demanding.”

In addition, Kang’s experience in a liberal arts program at UCLA helped foster a sense of appreciation for other fields of study, and a general desire for learning. “This gave me the discipline, but more importantly the passion, to pursue continuous learning that is essential to keeping up with the field of data science,” he says.

Kang also notes that starting from a non-technical background helps him empathize with non-technical stakeholders, which he uses to communicate effectively in his role.

Employment history

Kang’s first exposure to working in data science came in an internship with the entertainment company Viacom (now Paramount). He spent seven months working as a data scientist intern. “This was my first real experience with data science in the industry,” he says. “I worked on predicting box office revenues.”

The experience was instrumental in helping Kang bridge the gap between academia and industry. He was able to identify the gaps in his skill sets that he would need to close in order to succeed in applied data science, he says.

In 2018, Kang joined the media company Forbes as a data scientist, focusing mainly on building recommendation systems. One example was a system that recommends trending news articles to writers in the newsroom.

“There was a heavy emphasis on back-end engineering, and it gave me an opportunity to better improve my software engineering skills,” Kang says. “It was also an opportunity to experience the end-to-end lifecycle of delivering a data product, from setting up the back-end infrastructure, to parsing insights from the data, to surfacing those insights to the end user.”

To be effective in his role at Forbes, Kang needed to have a solid grounding in Python and software architecture.

After about three years at the company, Kang joined Uber as a data scientist in a role heavily focused on product analytics. “I worked specifically on merchant growth and acquisition. This meant that the deliverables were focused more on informing business decisions and making product recommendations." Kang notes that data engineering was also a significant part of the role. "Data from a multitude of sources had to be consolidated to properly communicate the state of the business.”

At Uber, Kang says he has had to be well-versed in experiment design, “which forms a core part of Uber’s principles in making data-driven decisions.”

A data scientist's typical workweek

“Meetings, unsurprisingly, are a key part of the week,” Kang says. “These are opportunities to deliver reports, presentations, and build empathy for stakeholders.” Oftentimes these stakeholders are product managers, though it is not uncommon to collaborate with other job functions such as user experience researchers, product designers, or engineers.

“Depending on the projects at hand, the rest of the time could be spent doing analytics—for example running descriptive analytics to prepare a monthly performance report or diagnostic analysis to investigate a change in a metric—crafting presentations, or more specifically defining the narrative and arriving at recommendations," Kang says.

Memorable career moment

“One of my favorite memories from my time at Forbes was from mentoring a team of graduate students through their capstone project as part of an industry outreach program,” Kang says. “It was refreshing to play the role of mentor for the first time, and it was as much a learning experience for me as it was for the students. That the team also won first place in the end-of-semester capstone showcase competition was just the icing on the cake.”

Career advice

“Fortune favors the bold,” Kang says. “Many things seem insurmountable at the onset but will ease with time and repetition. Also, it’s important to know the difference between a positive and negative challenge. Quitting the wrong pursuits enables us to focus on the things that matter.”

Practically speaking, Kang recommends anyone interested in data science should start by learning Python and statistics. "If you’re undeterred and curious enough, you will naturally fall into the fields of data science and machine learning next."

本文主要内容转载原作者为Bob Violino，仅供广大读者参考，如有侵犯您的知识产权或者权益，请联系我提供证据，我会予以删除。

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