6 Key Career Strategy Tips for Data Scientists
You probably don’t need us to tell you that this is a remarkable time to be a data scientist – you already know. With the demand for data-driven decision making only increasing, and the scramble for talent growing as more traditional firms get on board, the market is becoming richer with opportunities, especially for those who are proactive about managing their career.
Despite the rosy picture that this paints for many of you, it is still very important to be strategic about your career. With so many different opportunities and so many people approaching you, it is critical that you keep your career goals in mind when evaluating offers and companies.Here is some advice for navigating the current landscape and managing your data science career:1. Plan your career carefully. Don’t jump ship just for money or just because you got a call from a friend about an “amazing opportunity”. Evaluate career moves based on how you can learn, grow your skill set, and position yourself to achieve long-term goals.2. Consider the level of support data science has organization-wide. Your success will depend on your opportunities to present your findings and solutions to senior leadership. If the quantitative team does not have leadership support, then you will constantly be fighting an uphill battle. You don’t want to spend most of your time trying to sell your ideas (and not actually implementing them) only to get nowhere.3. Make sure the organization (or team) has the funding and patience to see the fruits of your labor – it won’t happen overnight.4. Be realistic about startups. Very few startups become profitable, let alone reach the success of Facebook or Uber, and most fail within a few years. Evaluate opportunities pragmatically, and be prepared for the very real possibility that it will be a losing venture. If you do try one and it fails, be very careful about trying it again. Never do it a third time.5. Build your business knowledge. The number one complaint we hear from companies about data scientists is that they lack business knowledge and skills. Always keep in mind that your analysis and presentation must be relevant to the company’s business goals. Your focus should be to develop actionable insights that the company can monetize, not just chase down cool or interesting problems. Always keep the business’ goals top of mind, and learn as much as you can about your industry. It’s important to be able to distinguish what’s important from what’s interesting.6. Develop your communication skills. Presenting findings to a non-technical audience (such as the marketing team or the C-Suite) is a crucial part of being a data scientist. Practice and hone your ability to communicate and present just as you would with any technical skill. Data science can be complex and hard to explain, so you will need to develop your storytelling ability and boil it down to the necessary details.