Five Steps to Advance Your Data Science Career (2)
Ling Zhang
Founder | AI & Data Science Strategy Consultant | Leadership Coach | Financial Consultant | Entrepreneur
In the last post, Five Steps to Advance Your Data Science Career (1), it discussed 2 Steps:
Now let's discuss the step 3 to step 5 as below
Step 3: How to solve business problems
As the goal of doing data science is to solve business problems and improve business performance, so you must focus on business impact not just do cool work.
Once you gain the hard skills, now it’s time to develop your business domain knowledge by solving real business problems. You need to learn business processes, operations, and contents, identify new opportunities from real world problems, define the right metric to measure data science outcome and interpret result by using a language that management team or leaders can understand without knowing the science and technology terms and bridge the gap between actual vs predict performance.
During the process, be aware to develop your soft skills – business skills. Develop close relationships with business stake holders and strive to understand the challenges they face on a day-to-day basis. The more you relate data back to what is important to them, the more likely you are to be viewed as a strategic partner and thus pulled into projects earlier on. If you adopt the mindset that stakeholders are your customers and you are trying to get them to buy and use your talent, you will become a more effective partner. Meanwhile, constantly exchange insights with peers and learning from their experiences.
Solve Business Problems?help you develop the right business metric and communicate result and avoid bias.
Step 4:?Prioritize and align your effort with business and your end position
Once you get some experience on solving business problems with some science methods and technology tools, you should be aware your end goal: where you want to end up in the career so you set up the right balance on your effort among different functional areas.
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In the first few years, your efforts may be evenly distributed among science, technology, and business skill, and your learning curve may be linear. Once you lay the right foundation on the three areas, you can start a biased efforts toward your end goal so you can well align your effort with your end position and prioritize your focus on different areas: data, science or qualitative, product/service or business skills and leadership.
It’s wise for you to stop listening to some “influencers” giving wrong career advice about data science and analytics, or stop doing what everyone else is doing such as using pandas, numpy, and matplotlib to create something that have no value or writing the same R code or creating the same PowerBI dashboards. Instead you should think of your unique value proposition, chart your unique course.
Assume you want to become a data science leader.?You should get more time on developing your domain expertise, collaborative and communication skills so you bring deeper and more immediate impact. As more and more candidates enter the workforce, your advantage is in your specific area of industry knowledge and expertise. Your communication skills help you comfortably and effectively collaborate with others in a company. Having these skills also enable you to pursue opportunities on the side: anything from consulting or developing your own startup idea, or mentoring and volunteering.
Step 5: Scale your skills and differentiate yourself through continued learning
Now it’s time to scale your skills. You may consider using your existing skills to solving different business problems in different industry so your domain expertise will be expanded
You may focus on building your unique value proposition and differentiating yourself and creating unique values so you can stand out in a crowd.
Meanwhile, you start to take on new and different problems, continue learning new methods and build new skills and developing the best fit solutions for existing and new business problems.
A separate post will be published to discuss how to scale your skills to solve different problems and differentiate yourself from the crowd.
Have different thoughts? Please leave your comments and share your thoughts.
In the next article, I will discuss Step 3 to Step 5.
May you grow to your fullest in your data science career!