How To Build Your Portfolio As A Data Leader

career development Jan 14, 2021
Some people consider their professional careers as a destination for their goals. While for others, it's a journey. But definitely, as we progress in our careers, we are able to achieve some level of skill or stature in our chosen field of work. Including the level of achievement from different experiences in roles and companies as we climb the ladder.
 
Its process is always different for most people, as there are different companies or organizations within an industry, job types, geographies, or other varied aspects of your profession. Only one point of similarity is when every time we climb up higher, a portfolio comes very handy.
 

Why a portfolio is important?

A portfolio is a way more effective element to showcase your work, your skills, and your abilities. It can be public evidence of your data science skills in your job prospects so far. Even if you have a referral, the ability to show potential employers what you can do instead of just telling them you can do something is crucial.
 

How to build a compelling portfolio as a data leader?

If you're deciding to move up your career and engage on some job listings, here are steps on how to build a compelling portfolio.
 

Step 1 - Define Your Goal

Obviously, money is part of the goal but having it alone can be a less-driving force for you to see the end of where you want your career to be. Also, moving from one career to another that aligns with your goal can add fuel to your passion and motivation for success. It allows your potential employer too to understand what's your long-term vision and short-term motivation for the job, and aside from what the job needs. It should be a short statement, yet precise on what you are trying to convey. You can add this on the Objective or Professional Summary section of a portfolio, found at the top-most part.
 
Examples are:
  • Motivated data leader with 4+ years of experience in building models that fix problems. Enthusiastic to strengthen skills including machine learning, problem-solving, programming, and creative thinking.
  • Data Scientist with 6+ years of experience executing data-driven solutions to increase efficiency, accuracy, and utility of internal data processing. Seeks to become the new Data Leader for XYZ Inc.
  • Data Analyst with 3+ years of experience and a strong passion for using predictive modeling, data processing, and data mining algorithms to solve challenging business problems.
  • Problem solver and a numerate individual eager to work as a Senior Data Specialist with ABC Inc. Coming to bring proficient modeling techniques and advanced computer background to help the company further its mission.
 

Step 2 - Don't Forget Skills

List down your skills (soft skill, technical skill, tool skill) that align with your prospected job listing mentions. It will present evidence of your relevant skills and abilities, as well as suggest what you are best at. Outside of a job or client search, it archives your acquired key competencies. Having it all together in a portfolio can be useful as well during your yearly review, or helpful later if you decide to improve/develop in some areas.
 
Since data science leans more on technical skills, these skillsets can help you list down popular and relevant details that employers will most likely consider.
  • Big Data tools: If you’re applying for big data roles, you should show your experience in key technologies. It helps to prove that you have an awareness of what software/tools are being used to build a big data solution or project. Among the popular tool skills in this category that you can add are Hadoop, Spark, and Hive.
  • Data science languages: Knowledge of key languages in data science is the most sought and essential information in a career portfolio. If you have skills within this category, it is best to include tool skills such as Python, R, Java, Scala, and SQL.
  • Data visualization tools: Data visualization is a vital component of any data science project and role. Having this skillset can help you present your ability to visualize and communicate data effectively, along with collaborating with others as well as making insights accessible and useful to a wider scope. Some of the popular tool skills to mention in this area are D3.js, Excel chart, Tableau, and ggplot2.
  • Machine learning frameworks: Machine learning is continuously in demand, and adding a skillset from this category can do a lot to impress. Some organizations may hire you with a view to building out this capability.
 

Step 3 - Generate Project Expertise

Projects help define your experiences and demonstrate your technical skills. It can be a dissertation, case study, client project, or inter-organizational projects (IOPs) that can add to the interest of your potential employer. Moreover, it helps confirm your job competencies, the complexity of the problem you have solved or studied, domain expertise (i.e. data governance, building data infrastructure), and willingness to do possible future project engagements.
 
Furthermore, professionals in the data field have one ultimate goal — solve business problems. Thus, include projects (personal, commissioned, etc.) that have relevance or connection to the job you’re applying for. You should have at least one project or publication on your portfolio, and add as you see fit.
 

Step 4 - Present Relevant Experience

This section will be the core of your portfolio. Make sure to customize it towards the job listing requirements, and list the most recent and relevant work experience. Be at ease if you have a few to mention since this part will be supported by the project expertise you have included. In case you have too many to mention, avoid putting irrelevant experience on the portfolio. Each entry should include your job title, the company, the period of time you held the position, and your accomplishments rather than your duties in that role. Keep in mind the consistency of formatting (bulleted or descriptive form). Through this, employers will see what you actually did, not just what you were supposed to do.
 
 

Step 5 - Add Social Media Links

Social media are used by employers as an extra evaluation factor for a potential job candidate. Through your social media links, you can add more compelling information about your career, which may include:
  • Screenshots of your previous work (i.e. projects, management work, campaign, or reports)
  • Kind of work you want to do and add your best work as samples. Your previous employers’ testimonials
  • Tells a more desirable personality to potential employers.
 

Step 6 - Review and Condense

Most employers want a concise portfolio without a lot of extraneous information, and only spend seconds reviewing it. The more compact a portfolio is, the easier it will be for the recruiter to review. In addition, if you're applying to a large company, there's a high likelihood that your portfolio will be printed out to be reviewed by multiple people, or at least shared electronically. It is best to have everything on one page which increases your own chances of standing out. It doesn't mean that you'll need to decrease the font size too. Rather, keep only the most relevant and compelling details related to the job you'd like to have.
 
 

Conclusion

Remember that employers can easily weed out thousands of applications, whether for senior-level or executive-level jobsThat's is why building a career portfolio requires keen attention to achieve the next step in our careers. Also, moving a career ahead may not be a straight-line because of changing circumstances. But with a strong goal and passion, there will be progress for the career success that we want. As we go forward in our careers, always amplify your skills and experience while growing as new leaders.
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