New Year Career Tips for Data Scientists


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by Alex White

Senior Partner, Talent & Research 5th Jan 2017

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Alex White is the founding partner at Avellio. He is an industry experienced former analyst, and has led over 120 retained search assignments for data science leaders, with mandates entrusted by leading global investment banks, major retailers and analytics consultancies.

a.white@avellio.com


As a successful data scientist or senior analyst, you will already be well aware that the labour market is treating you very well right now, especially from the point of view of being in a privileged position of practically being able to pick and choose where you want to work. Since 2012, there has been a steady increase in the number of opportunities available to you, but its important not to become too 'complacent'.

The analytics market has changed shape significantly over the past five years, with this evolution its essential that data scientists and analysts are calculating about the career choices they make to ensure career success. Whilst there are a great number of outstanding companies and organisations for you to choose from, on balance there are also more newly qualified and experienced professionals entering your space, which means that many companies and brands are becoming a little more selective about who they hire. 

Leaning on our experience with managing hiring situations between clients and candidates, we are equipping you with ten essential pointers aimed specifically at data scientists and analysts, to help you find your way through today's hiring maze, and to optimise your chances of securing your next dream job!

1. Take control and learn to be a little more 'hard-nosed'

There are zero rules about commencing discussions and negotiations with multiple companies simultaneously, and refining your focus based on comparing the experience and connecting what you hear and see during the process with your pre-determined career objectives. In fact, this can be a very positive exercise, as by analysing multiple job specifications you can narrow down your area's of core interest in terms of tools used, methodologies employed, types of data sets that you will be exposed to, etc, whilst simultaneously seeking to align yourself with each hiring companies commercial objectives and values, and so on. Furthermore, it will also serve as a guide that you are learning the data science tools or analytical methods that are most in-demand according to the roles that you applying for.

2. Establish your 'why' for making a career change, and stick with it

As you move along the interview process and begin to seriously consider an offer of employment from one of the companies on your short-list, ensure that you defer back to your original objectives and motivations for seeking the change, was it that you wanted to further develop your machine learning skillset? Or are you attempting to move in to a completely new industry and sector, one that fascinates you deeply? What flavours of new data do you want to gain greater exposure to? Its crucial that the job opportunities that you are applying for support the original pathway you want your career to venture in to, combined with a good firm cultural connection.

3. Make a decision

As the market is so buoyant, it’s essential to make firm decisions. Data science and analytics teams are building-out rapidly and looking to hire talent as quickly as possible, so if you really like the look and feel of an opportunity, and you've scoped it our thoroughly, then why not act on your decision and accept the offer? If you procrastinate for too long you may either lose the confidence and interest of the hiring company, or miss out to another candidate altogether. 

4. Don't let money be the exclusive measure of the job

It is a natural motivation to find yourself inclined towards accepting a job offer according to a higher salary being offered. Salary is obviously a key component in the decision making process, but also consider placing a value on learning and development, whilst also being mindful of your long-term goals. 

5. Look beyond the vanity of a job title, be prepared to ask qualifying questions

Since titles in data science and analytics roles can vary wildly from company to company. It can be hard to establish from title alone if the responsibilities, seniority, and the position itself are in line with where you want to be. Asking questions about who the role reports to, responsibilities, tools and methodologies used, types of data, and how much time is spent on different aspects of the role (analysis vs. data management) can all give great insight into whether the role will be a good fit. Since titles can vary so much, accepting (or rejecting) an offer based solely on the title might not be your best move.

6. Data science skills are becoming more niche, know your strengths. 

Increasingly data science and analytics roles are becoming highly specialised. As established data teams are growing larger, we’ve seen roles become more specialised, with the focus being less on “superheroes” who can complete every task, and more on hiring multiple specialists with different backgrounds who can collaborate as a team. This might mean that you’re not be a fit for every role, but don’t let that discourage you! With more opportunities than even before, you’re bound to find something that’s a good fit.

7. Identify your skills gaps, formulate an action plan for addressing any deficits.

With this trends towards specialization, you may notice that some companies have an extensive list of skill requirements they’re looking to find, and seem unable to compromise if you’re missing even one of them. Others may be more willing to mentor, and look for potential to develop an employee. This can sometimes depend on whether or not the role you’re applying to is replacing someone who has left (a backfill), and the position needs someone who is all ready to hit the ground running. For a role that is completely new, a company might have more leeway to teach skills on the job. If a company can’t compromise, this might not necessarily be a reflection on you.

8. Prepare and practice your 'pitch'

With analytics and data science moving “from the back room to the board room”, it’s becoming much more common to see employers testing potential hires on their ability to present technical findings and quantitative insights. Presentations have become much more common in interviews, even for highly technical positions, so earlier this year we put together a list of tips on how you can prepare and make sure your presentation is striking a good balance between technical details and business insights.

9. Anticipate practical tests and assessments based on your experience

Another outcome of the increased interest in the data science and analytics fields is that companies are looking for better ways to evaluate technical skills. Anyone can put “Data Scientist” on their resume, but employers want to know if your R or Python skills are actually robust, or if your only experience was a single college class a few years ago. Be realistic about the tools on your resume, and know that you may be given real-time tests or practical assessments so that you can demonstrate your proficiency.

10. If you’re unsuccessful in securing your dream job, get feedback!

With the market becoming more competitive, and roles becoming more specialized, it’s unlikely that you’ll be a perfect fit for every role. If you’re turned down, see if it’s possible to get feedback on how you can improve. Sometimes the feedback may have more to do with a company’s specific vision for the role, or other times it may be specific skill areas where you can improve. It’s a great opportunity to become more aware of your own skillset and to use the feedback for career development.


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Peter Zolobko