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Editor’s note: The Towards Data Science podcast’s “Climbing the Data Science Ladder” series is hosted by Jeremie Harris. Jeremie helps run a data science mentorship startup called SharpestMinds. You can listen to the podcast below:

编者按：迈向数据科学播客的“攀登数据科学阶梯”系列由杰里米·哈里斯(Jeremie Harris)主持。 杰里米(Jeremie)帮助运营一家名为 SharpestMinds 的数据科学指导创业公司 。 您可以收听以下播客：

It’s no secret that data science is an area where brand matters a lot.

数据科学是品牌至关重要的领域，这已经不是什么秘密了。

In fact, if there’s one thing I’ve learned from A/B testing ways to help job-seekers get hired at SharpestMinds, it’s that blogging, having a good presence on social media, making open-source contributions, podcasting and speaking at meetups is one of the best ways to get noticed by employers.

实际上，如果我从A / B测试方法中学到的东西可以帮助SharpestMinds招聘求职者，那就是博客，在社交媒体上有很好的影响力，做出开源贡献，播客和聚会上讲话是引起雇主注意的最好方法之一。

Brand matters. And if there’s one person who has a deep understanding of the value of brand in data science — and how to build one — it’s data scientist and YouTuber Ken Jee. Ken not only has experience as a data scientist and sports analyst, having worked at DraftKings and GE, but he’s also founded a number of companies — and his YouTube channel, with over 60 000 subscribers, is one of his main projects today.

品牌很重要。 如果有人对数据科学中品牌的价值有深刻的了解，以及如何建立品牌，那就是数据科学家和YouTuber Ken Jee。 肯(Ken)不仅曾在DraftKings和GE担任过数据科学家和体育分析师的经验，而且还创立了许多公司-他的YouTube频道拥有6万名订阅者，是他今天的主要项目之一。

For today’s episode, I spoke to Ken about brand-building strategies in data science, as well as job search tips for anyone looking to land their first data-related role. Here were some of my favourite take-homes:

在今天的这一集中，我与Ken谈了数据科学领域的品牌建立策略，以及为任何希望担负起与数据相关的第一个角色的人提供的求职技巧。 以下是一些我最喜欢的地方：

• Many companies actively look for candidates who embrace data science not only as a tool, but as a way of looking at the world. That’s because a good data scientist needs to be able to solve data science problems, but also spot them — and that means learning to think see the world through the lens of data.许多公司积极寻找不仅将数据科学作为工具，而且作为一种看待世界的方式的候选人。 那是因为优秀的数据科学家需要能够解决数据科学问题，但同时也要发现问题-这意味着要学会思考从数据的角度看世界。
• As one example, we discussed the idea of treating the job search like a data science project: clustering job descriptions with NLP to identify roles that might be worth applying to, and critically, measuring your application conversion rates. That last trick is key: conversion rates are the only way you’ll know how much work you need to put in to generate your target number of interviews each week.作为一个示例，我们讨论了将求职视为数据科学项目的想法：将工作描述与NLP聚类，以识别可能值得应用的角色，并至关重要地衡量您的应用转换率。 最后一个诀窍是关键：转化率是您知道每周需要多少工作才能产生目标面试次数的唯一方法。
• It’s important to recognize the aspects of the job search that you can control, and those that you can’t — because not doing so can cause you to blame yourself for failures that were unavoidable. For example, an interviewer’s low blood sugar might cause them to be unduly harsh, and a hiring manager who got up on the wrong side of bed on the morning of your onsite might fail to connect with you as well as someone he interviewed the previous day.重要的是要认识到您可以控制和无法控制的工作搜索方面，因为不这样做会导致您将自己归咎于不可避免的失败。 例如，面试官的低血糖可能会导致他们过分苛刻，而在您现场早上起床时坐在床边错误的招聘经理可能无法与您以及前一天面试的人保持联系。
• Research the companies you apply to. You should arrive at your interview with a clear idea of what the company does, and some educated guesses about what their biggest problems might be. Bonus points if you come prepared with questions about how some industrial or economic trend is affecting their business! (hint: COVID is a big factor for many companies)研究您申请的公司。 您应该在面试时就公司的运作情况有一个清晰的认识，并对他们最大的问题可能是一些有根据的猜测。 如果您准备对某些工业或经济趋势如何影响其业务提出疑问，即可获得加分！ (提示：COVID是许多公司的重要因素)

You can also follow Ken on Twitter here and check out his YouTube channel here to keep up with his work. Also, check out Ken’s latest 66 Days of Data initiative here!

您也可以在Twitter上关注Ken，并在此处查看他的YouTube频道以跟上他的工作。 另外， 在这里查看Ken最新的66天数据倡议 ！

You can follow me on Twitter here.

你可以按照我的Twitter 这里 。