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In the previous chapter, we examined the various strategies you can employ for finding a job opening, expanding your professional network and gaining insight into the industry. However, all this is of limited effectiveness if not accompanied by the right attitude and self-presentation skills. Note that this is more than just writing an appealing resume (which can easily be outsourced nowadays) and a good cover letter (which can also be outsourced!).
Presenting yourself is all about having conviction and conveying this efficiently without much vocal communication. It’s what image-makers do for their clients, and since chances are you don’t have the budget to hire one, you will need to do their job yourself! Note that the conviction and air of confidence that you need to portray must be based on having solid abilities, so if your skill-set is limited, you will need to work on that first (see Chapters 8 and 12).
In this chapter, we’ll look into several guidelines about presenting yourself: in your cover letter, on the phone and, of course, in person. We’ll examine the importance of focusing on the employer and his company’s needs, the value of flexibility and adaptability, the significance of the deliverables in a data scientist role and how you can guarantee them, the ways you can differentiate yourself from other data professionals (who may be after the same position), the value of being self-sufficient as a professional and a few other factors you may want to consider about improving the way you present yourself.
The advice in this chapter is applicable in other fields as well, not just data science. However, you need to employ at least some of these strategies if you want to have a fighting chance of making it past the first stages of the interview process.
14.1 Focus on the Employer
The alpha-male approach that has been dominant in job hunting for many years may not be the most effective strategy when it comes to landing a data science job. Of course, it is great if you are a go-getter and exhibit a strong, somewhat aggressive approach to tackling problems and making things happen, but this may not be what an employer is looking for. With all of your social media information at his disposal and a sense of uncertainty about the technical jargon on a data scientist resume, your potential employer may feel overwhelmed if you start shooting big data technical terms at them out of the blue.
You need to focus your whole approach to the employer (i.e., what’s in it for them) and communicate in terms that they can understand in an unambiguous manner that shows knowledge and confidence. You want to avoid sounding like you are technically savvy with no people skills because they’ll think you are just a geek. If you come across as overly confident, they may think you are playing them. What they need is someone who is upright, balanced and cares about the company; someone who will go above and beyond just what is on his job description. Can you be that person?
Few people are naturally charismatic marketers, and since you are not in the marketing game, chances are marketing yourself is not your strongest suit. The only way to overcome this is through practice. You’ll need to accept that you may not get one of the first few positions you apply for due to your lack of experience unless you are such a great fit for the job that the employer is willing to overlook that. It’s a small price to pay for the opportunities that practicing your interviewing skills will open for you later. Focusing on the employer will be useful not only for the various stages of the hiring process, but also for performing the actual job afterwards. You don’t need to be a data scientist to figure out that having a good relationship with your manager benefits you, your employer and everyone else you’ll be working with. A healthy relationship can only bring about good things for your career as a data scientist and for your resume.
14.2 Flexibility and Adaptability
We talked about flexibility and adaptability briefly earlier in the book (Chapter 4), emphasizing their importance in the data scientist mindset. However, here they are described in a different light.
Flexibility and adaptability are all about how you can stretch and adapt your skills and experience to fit a job description and its requirements as well as how you can amend any gaps in knowledge you may have. They can be demonstrated by being honest about what you know and explaining how the skills you have can be adapted/enhanced to meet the firm’s needs. This also shows your creativity and interest in enhancing your skills to benefit the company.
For example if you know R or Matlab, you can adapt from one to the other quite quickly, and if you are flexible enough, you can use either one to get the job done. Despite their functions being somewhat different, the underlying logic of the two data analysis tools is pretty much identical, so shifting from one to the other is quite feasible.
Flexibility and adaptability are also important when it comes to selecting the positions you wish to apply for. Say you are looking for a standard data scientist position, but all you have a shot at is an entry-level (junior) data scientist post. Will you go for it? If you are flexible enough, then yes. Besides, experience is experience. There is no doubt that it’s better on your resume than Kaggle competitions and practicing on benchmark datasets.
If you can only find a data scientist position in a domain you are not familiar with, you can still demonstrate your adaptability by becoming familiar with that industry and using your data science skills to tackle its problems.
14.3 Deliverables
So you know all the relevant software and you’ve read your statistics and machine learning books so much that you’ll have a hard time reselling these books, but does that mean that you can do the job and do it well? It all boils down to the deliverables involved.
The deliverables of a particular data science position may vary significantly since different employers have different business needs for their (big) data, which differs significantly from industry to industry. They may want you to undertake a project management role—if not right from the start, then a few months down the road. This is not uncommon for a senior data scientist position (business data scientist type). You may know your stuff well, but at the end of the day, your future employer needs to make sure that you won’t be sitting in front of your workstation all day and that you’ll exhibit some human resource management skills. After all, you have good communication skills, right? So what’s stopping you from becoming a project manager or an assistant team leader?
A potential employer is looking for what can you bring to the company if you are hired. You can say that you are able to deliver every single item listed in the responsibilities section of the job description and explain exactly how you can do that. But you can also be a bit more creative and bring some new ideas to the table, preferably something that you have thought through beforehand. Step into the employer’s shoes for a minute and evaluate the two possibilities from their point of view. Would you hire you?
The deliverables factor is something that ties in with each one of your skills, too. You didn’t learn R because of its pretty interface, nor did you learn Hadoop because of its nice documentation and you certainly didn’t learn Java because of what its fans say about it. You learned each one of these programs because they can deliver something valuable to you and bring usefulness to your work. So when you have a chance to talk about your technical skills, you should point out how they can benefit your potential employer because that’s what he will care about the most. Remember subchapter 14.1 and the importance of focusing on the employer. Your interview is your chance to apply what you’ve learned and convince him that you have something to offer that he would be unwise to pass on.
The same applies to your other abilities, the so-called soft skills. In truth, there is nothing soft about them because if you use them well, they can have some really hard effects that will benefit everyone around you. Sure, there is a certain prestige around knowing a particular piece of software at an expert level, but being able to communicate well can be as important, if not more so, depending on the particular position. You can learn a piece of software in a few months, so even if you don’t know how to use the big data package that a company prefers, that’s not an issue as long as you’ve worked on similar software. However, you need the ability to communicate well right from the start. During the interview process, you want to show that you can use your soft skills to provide lots of deliverables because that could be what distinguishes you from all other applicants.
14.4 Differentiating Yourself from Other Data Professionals
Distinguishing yourself from the competition is essential when seeking a data scientist placement. Some of your competitors will be people who are worthy data professionals who have done some studying, taken a couple of courses and decided to brand themselves as data scientists. They may have no idea about the scientific method, the data science process or any of the qualities that constitute the data scientist mindset, but this gap of know-how and thinking is not reflected on their resumes. So how can you differentiate yourself from them and demonstrate that you are a real data scientist who can do what it takes to make their big data talk?
Let’s look at the points you can emphasize to distinguish yourself from your competitors in an unambiguous way:
- Machine learning experience. As a data scientist, you can do more than t-tests and correlation analysis as you have a good grasp of machine learning techniques, both in theory and in practice. This translates into intelligent and very efficient data processing that can yield promising results without the use of any ad hoc models.
- Big data know-how. Obviously, your expertise extends to the distributed computing domain and you embrace big data, knowing how to tame it with the relevant technology and know-how. This, by itself, should give you enough differentiation and a strong competitive advantage.
- Strong communication skills. You are confident and skilled at telling a story about your findings through the data science process you follow because you understand everything in more depth (even if you are not the most experienced person). This should be evident in the way you present yourself during your interviews.
- Scientific approach to data analysis. With enough data, you can draw all kinds of conclusions and find a lot of interesting relationships in the data. In fact, you can find statistically significant results in completely useless combinations of variables. This doesn’t mean that anyone cares about such results. Your selling point is that your results are driven by meaningful questions that you ask beforehand (when you formalize your hypotheses), and every step of the analysis is based on a methodology that is scientifically sound and can be easily replicated.
- Familiarity with data analysis tools. Some data professionals will be familiar with a lot of the software that you use too, but they are less likely to know the ins and outs of R, Matlab and other data analysis tools, something that could give you an edge. You can perform data analysis tasks using Java or Python, but the aforementioned data analysis tools are exceptionally good for data exploration, data discovery and, to some extent, data visualization. Knowing both programming and data analysis tools gives you a clear advantage.
- Other factors. There are other small things that may differentiate you from would-be data scientists, some of which are too insignificant on their own but together make up something powerful and significant. These factors have to do with the data scientist mentality and several other qualities that you possess and may take for granted most of the time (e.g., problem-solving, ability to think outside the box, ability to come up with effective ways to quantify qualitative data, etc.).
Differentiating yourself from wannabe data scientists is one thing, but what about differentiating yourself from other data professionals (e.g., data architects) who have good technical skills and may be well known in the industry? What makes you different from them and more suitable for the particular role you are pursuing? Why would someone care about your additional skills and not about their additional experience? These are questions you need to address first for yourself and then for the potential employer if you really want that job.
Let’s take a look at all the factors that differentiate you from the other data professionals (database administrators, business intelligence analysts, etc.) in an unambiguous way:
- Data analysis know-how. It is not uncommon for data professionals to have limited knowledge of statistics and/or machine learning, things that are your bread and butter as a data scientist. Even if your employer is not that knowledgeable in them, they should still appreciate the benefits and inherent value of your skills in the big data world. Remember that many of them still think of data scientists as statisticians who can handle big data; you need to explain how much more your training and skills can accomplish for them.
- Big data know-how. Your familiarity with big data is a huge plus since most data professionals don’t know the relevant technology.
- Strong communication skills. As in the previous case, this is an advantage when it comes to working in the modern business environment, which is quite diverse and communication driven.
- Familiarity with data analysis tools. Even if your competitors know a few tricks about using statistics and machine learning on various datasets (pretty much everyone has heard of clustering, for example), your comfort and experience with specialized data analysis packages such as R and Matlab will allow you to produce meaningful results for your potential employer faster than individuals who don’t have experience with the packages.
- Scientific approach to everything you do. You are quite familiar with the scientific method, even if you don’t realize it. This could be used to your advantage since many employers value a methodical, disciplined and organized approach to tasks.
- Other factors. There are several other smaller factors that may distinguish you from other data professionals. They are related to the data scientist mentality and other qualities that you may have and take for granted (e.g., being able to see what data is useful but not there, having the ability to come up with useful models, employing a more creative approach to problem solving, etc.). Even if your employer is not savvy when it comes to data science, these things will come through if you are aware of them and value them enough.
14.5 Self-Sufficiency
The definition of self-sufficiency used in this book is “being independent in a proactive and somewhat creative way.” It means knowing what needs to be done and doing it with little to no guidance, especially when it comes to your own domain. You need to own it and plan it accordingly.
Like most things you talk about on your resume, in your cover letter and during networking sessions, you need to be able to demonstrate your self-sufficiency with examples drawn from your professional experience by referring to specific cases where you participated in or led a project, taking initiative and showing creativity. Finding an innovative approach to a problem, developing a clever feature in a data analysis package or handling a difficult situation through a creative approach, all without relying on a supervisor, are examples of self-sufficiency. This is fairly common in the research industry although it is not valued as much as it should be. The same initiative in industry could result in a raise, a bonus or perhaps even a promotion, while in the research world it is usually taken for granted. So if you are in research, it is high time you learned to value this attribute of yours and sell it properly to an employer who can appreciate it.
14.6 Other Factors to Consider
Interview-appropriate personal presentation, language, physical appearance, etc. are also important factors but are beyond the scope of this book. There are many books and websites that address the personal and interpersonal aspects of interviewing. You would be wise to take advantage of the advice that is available from these sources. Some of them can be found in Appendix 1.
14.7 Key Points
- Presenting yourself is more than just writing a good resume and a nice cover letter. It entails a lot of things that refine your first impression, whether this is via a letter, a phone call or a face-to-face meeting with a potential employer.
- Focusing on the employer is important to keep in mind when presenting yourself for a data science job. Specifically, you’ll need to understand what they require from the use of big data, listen carefully to what they expect from the person in the position they are hiring, be able to explain to them what you can offer in terms of benefits for the company and the bottom line and communicate effectively. Ask lots of questions and show a genuine interest in the company and the position.
- Flexibility and adaptability can be demonstrated by being honest about what you know and explaining how the skills you have can be adapted or enhanced to meet the organization’s needs. This also shows your creativity and interest in developing your skills to benefit the company.
- Deliverables relate to what you need to deliver to fulfill the requirements of the data scientist position in which you are interested as well to the effect your specific skills and know-how can have on the bottom line of the company that may hire you. The deliverables can also refer to other benefits you can bring to the organization such as initiative, ideas, improvements in their existing BI processes, etc.
- Differentiating yourself from your competition is very important in this field. It involves selling the specific technical skills, know-how and non-technical skills you have that make you stand out from wannabe data scientists and other data professionals (database administrators, business intelligence analysts, etc.).
- Self-sufficiency is a must-have quality for any profession nowadays, but especially in data science. It means owning your work, acting responsibly, showing initiative and managing your workload without relying on a supervisor. It is highly valued and a great ability to promote about yourself.
- For other factors that are important to keep in mind when presenting yourself for an interview, see books and articles on the subject. Such factors include:
- physical appearance
- language (including body language)
- business cards
- research (learning about your audience before meeting with them)