While Data scientists are much sought after in demand today across IT verticals, the challenge lies in providing value and Data Scientists are among the most in-demand amongst tech professionals, but they must avoid some common pitfalls to be successful in their career to be successful at work from our observation. Named as one of the best job in America for the consecutive last four years, data scientists enjoy high job demand parity and lucrative salaries, in exchange for their skillsets in big data analysis, machine learning, coding languages, algorithms, and problem assessment of which we are able to assist our customers to a large extent successfully.A part of the reason for the great demand for data scientists at nearly every company is the rise of the consumer-driven market, a global managed services provider and rise of technology consulting firms. With the power shifting to the consumers, there's increased competition among companies to be better with niche outcomes and there's pressure to be the better option for their customers among the multitude of existing choices. Within this scenario today, there's not much space for building decisions on experience or a 'gut feeling' the market is changing and so are the trends and there is very limited room for mistakes. Companies are now to keen to use data and statistics that will both improve how they serve their customers and their own internal problems at ease.
With a rounded skill set and the flexibility to fit in any team namely Sales, Marketing, Advertising, Pricing, Inventory, E-commerce, etc companies naturally want someone who understands their business in a broader sense. Data scientists fit perfectly in this scenario as they work as a bridge between information technology and business to come up with solutions built on the internal and external factors and data at an optimal cost and reduced technology and platform dependency. With so much pressure to help a company perform, certain common mistakes crop up for data scientists. Here are three mistakes data scientists often make, and how to avoid them, according to our understanding.
1. Starting with bad data
Data that is incomplete or biased needs to be fixed before it is used. A company that makes major decisions based off of inaccurate or biased data sets will be setting themselves up for failure. Word of Advice: Carefully examine and clean data before using it in any analysis.
2. Allowing budgetary or timeline restrictions to hurt the work
Data scientists are not always able to use the best tools and methods available due to budgetary and/or timeline restrictions. Give them freedom with time based outcomes. The budgetary/timeline restrictions are more common than you think in the industry. Companies want solutions and they naturally want them fast. It's now up to us to balance all the expectations to come up with long-term, quality solutions quickly, and this does result in missing scenarios in the assumptions or spending more time integrating the work being done or even in communicating the progress. Advice: Learn to communicate problems and progress effectively with both technical leads and business leads to keep projects on track.
3. Not understanding the business
For younger data scientists in particular, a lack of domain knowledge can be a hindrance to the work being done. Skilling them on all fronts become pivotal. Data scientists must have both the business and technology understanding to be the best as what they do.
Advice: Spend time collaborating with colleagues instead of working only on your own to develop a well-rounded understanding of how data is being used across departments. Spending time studying the domain (retail, healthcare, finance, etc.) and interacting with the day-to-day operations of a business helps us understand the data better.
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