Growing In Your Data Science Career

The start of your career as a Data Scientist / Analyst is an exciting time as you get to use your acquired skills to dig through real world data and uncover valuable insights for an organisation.?

While some level of growth will naturally occur on the job, taking intentional steps to develop and expand your skillset is the best way to maximise your career growth and advancement.?

Specific skill set areas you can target include :?

• Technical?
• Data Storytelling / Presentation
• Industry knowledge
• Collaboration / Leadership?

Everyone is unique and has a mix of skill sets with varying degrees of competence. Some might be stronger in their technical abilities than in their presentation skills and vice versa. Along your professional journey, identify which areas you are competent in and those in which there is room for growth; this can be done through self evaluation and soliciting feedback from your peers and leaders. Even in the areas you are most component, there is often still room for those skills to be further refined.?

Below are steps you can take to grow in each of the above listed skill set areas.

Technical

Given the diverse range of business problems and the development of new technologies, there is a lot of opportunity for existing technical skills to be sharpened and new (technical) skills to be acquired when you enter the workforce.?

What steps can you take to grow in this area??

• Stay up to date and acquire skills in in-demand tools / technologies. Years ago, Python and R were not as widely used as they are today. The same goes for dynamic visualisation tools like Power BI and Tableau. Aim to master at least one programming language (preferably python) and one visualisation tool (Power BI / Tableau), while not neglecting SQL - the language of databases.?
• Take on work projects that require you to learn and use data analysis techniques that you are not familiar with or techniques that are different from what you have mostly used. For example, during the first year of your career your projects may have been trend analysis focused which could have included looking at the growth/decline in a customer base by different regions over the past year. To broaden your expertise, you could request to take on text analysis projects where you are identifying major themes in written customer feedback (e.g. product quality, product pricing etc.) and their sentiment (i.e. is the customer feedback positive or negative?).
• If you are a Data Analyst with skills in descriptive analysis, consider expanding your skillset to include the predictive. In my article, Building A Data Science Team From Scratch, I highlight the skills of a Data Analyst and a Data Scientist.

Data Storytelling / Presentation

Out of excitement and eagerness to deliver projects, some Data Scientists / Analysts early on in their career tend to share their results (graphs, tables etc) with little to no interpretation, leaving it to their business stakeholders to do so.?

It is however the responsibility of the Data Scientist / Analysts to interpret the results, recognise connections between them, and communicate the insights in a clear and understandable way. This is a critical part of their work.?

What can you do to grow in your Data Storytelling / Presentation skills??

• After generating results (graphs, tables etc), spend time mulling over it. As you do so, you will not only draw more and clearer insights but will also start making connections between results that initially may have appeared disjointed. Include time for interpreting the results and creating your presentation deck in your project execution timeline.?

• Practice your presentations. Practice your comments aloud as if you were presenting to an audience or practice with a colleague before the actual presentation. Aside from a smoother and clearer delivery of the presentation, you might identify new insights and their connections to previous findings when you practice. I have personally experienced this many times. Note: Everyone is different. Practicing aloud does not work for everyone but I believe at the very least it is worthwhile reflecting on what you will say during a presentation.?

Whenever applicable and as much as possible:

• Use simple visualisations (bar charts, line graphs etc) as it is easier for an audience to absorb information that way. If you have results in tabular form, always consider whether it would be easier to grasp as a visualisation.?

• Avoid using technical lingo with non technical stakeholders. Use clear and straightforward language that everyone can understand. For example, saying “on average, the error in the model's cost predictions is 5,000 Ghana Cedis” is more straightforward compared to “the Root Mean Square Error for the model’s cost predictions is 5,000 Ghana Cedis”.?

• Present your findings in a slide deck (e.g powerpoint, canva etc). This generally allows for a better flow of data storytelling and looks more professional and aesthetically pleasing as compared to presenting your findings in a coding (Python or R) notebook or even an excel file.

Industry knowledge

Although a Data Scientist / Analyst can work in any industry, having knowledge of an industry or domain is beneficial. For example, it could help you come up with ideas for valuable data driven solutions. All other things equal, a data professional who works in the vehicle sales industry and knows that vehicles can be categorised by type(SUV vs Saloon, Luxury vs Economy), engine capacity, fuel type etc. is more likely to be able to identify opportunities for data driven solutions compared to someone who does not have this knowledge.?

How can you grow in this area??

• Some organisations have free internal courses that employees can take to learn about the industry. Take advantage of that as much as possible. If that option is not available at your organisation, explore external online learning platforms like Coursera and Udemy. Some organisations finance external learning opportunities for employees.?

• Some Data Scientists / Analysts have the opportunity to work on projects for different departments (e.g marketing, sales, customer service etc.) in an organisation. Departments in large organisations usually have documentation that provide an overview of how they function and information on specific subject areas within their department. Request for and review those documents before starting a project. The information in these documents tend to come in handy when executing projects.

Collaboration / Leadership

Some Data Scientists / Analysts in the early stages of their career only execute projects, and their Managers lead the charge in collaborating with various stakeholders (business, IT etc.) throughout the lifecycle of a project. This includes leading discussions with business stakeholders to understand their business problems, identifying possible data solutions, and managing unforeseen issues as they arise in the project.?

One way to grow in your career is by progressing from only executing projects to additionally leading projects / teams which means growing in your collaboration / leadership skills. How can you do this??

• Firstly, observe how your manager and colleagues handle discussions with stakeholders. Are there particular questions they usually ask to get a clear understanding of the business need? How do they handle instances where there are differing opinions on how to solve a problem? Soft skills are key when collaborating!?

• It is not unusual for junior Data Scientists / Analysts to get some assistance (advice on approach, coding etc) from teammates. After a few months on the job, if you’re not already doing so, take the initiative to reach out to teammates for assistance. This is a simple first step in building your confidence and gaining experience in collaborating. From there, with the support of your manager, you can build up to leading internal (data science) team meetings, and subsequently meetings with business stakeholders outside your team.?

• Consider joining a personal development group like Toastmasters that help individuals build confidence and grow in their leadership and communication skills.?

• Take on opportunities to mentor data science interns in your organisation.?

Some general advice on career growth and advancement

Below are some general tips for your career growth and advancement.?

• Review the job description for the next level in your career. Start developing the skills that you do not have but are required for that position. Ask for the opportunity to take on some of the responsibilities for the next next level to gain experience and demonstrate your abilities.?

• Inform your manager of your personal development and career advancement goals so you receive the necessary guidance and support to achieve those goals.?

• Be open to feedback on areas of growth from peers and leaders. It is not always pleasant to hear but thoughtfully consider any given feedback and take action to improve if needed.?

Conclusion

“Success is where preparation and opportunity meet.”

Most professionals have a desire to advance in their career but that advancement is strongly tied to growth in skills; this growth occurs most effectively by taking purposeful steps.?

To all my fellow Data Scientists / Analysts, embrace a growth mindset! ?????

Aside from it opening doors for career advancement, there is a lot of personal satisfaction that comes with it.??

• What are the most impactful steps you have taken to grow in your career as a Data Scientist / Analyst??

Feel free to share your thoughts in the comments.?

Thank you for reading! I appreciate your time and attention. ??