One of the fundamental goals of a Data Scientist is to help your company make quicker and better decisions, so you can be at the top of your industry.
Employing a Data Scientist is beneficial when you need help to collect, clean, visualize, and most importantly, make sense of your organization's data correctly. By having a team of data experts working with you, you’ll be able to make better business decisions based on the data you have and work your business through different data pipelines. This, in turn, will help you make better business propositions, better products, and ultimately attend to your customers’ needs better, leveraging your competitive advantage.
Let’s take a look at why Data Science is important for your business, and what skills you should look for when hiring a Data Scientist.
Table of contents
Why is Data Science important for your business?
How do I hire the right data scientist?
Which skills should you look out for when hiring a Data Scientist?
Where do I find the best Data Scientist professionals?
The idea of a data-driven business is taking root deep in the mind of business culture, i.e., data culture is slowly becoming business culture. And if there was any time to get ahead of the curve, this is it. Here are the top 5 reasons why Data Science is so important for your business today.
1. Helps you expand your business
Top-performing companies are using data visualization tools in tandem with analytics tools to understand complex data. Businesses are looking for data scientists to help them adopt a data-driven approach. In fact, 59% of organizations worldwide are using big data analytics (MicroStrategy, 2020). A Data Scientist might help you find new potential markets interested in your product, and in order to identify new customers, you need to have a good sense of what your current customer base looks like. Data science may also spot new trends or figure out which inventory items will have the most impact on sales right away.
2. You can gain a better understanding of your customers
Customer behaviors are always changing, and without the help of a data scientist, it gets tough to keep track of them. Airbnb, for instance, which helps travelers and hosts find and rent housing spaces, recently studied user behavior during online searches and changed its algorithm engine to deliver more personalized results. Bookings and reservations both increased as a result. A developer could unearth this kind of insight about your consumer base's behavior for data analysis to better your company strategy.
3. You’re at the core of decision making
With businesses adopting a more data analysis approach, data science departments are becoming crucial to their sustenance. Hence, a data scientist working in your team will always help you make the best decision based on the next best action showed by the collected data, taking emotions and precedence out of the equation.
4. It improves forecasting
By using current and historical data, you can accurately predict future trends and forecasts. With the constant change of our everyday lives, Data Science can bring better insights by spotting patterns we might not always see. Forecasting allows you to make data analysis business decisions and establish data-driven strategies. Financial and operational decisions are based both on current market conditions and predictions of the future. Past data is compiled and examined to uncover patterns, which are then used to forecast future trends and changes.
5. Big Data is everywhere
If you look around, chances are a computer would be one of the first things you spot. If not a traditional desktop or laptop, a simple smartphone. By using these machines, we are creating an enormous amount of data every day. How? By simply twitting or sharing posts on Instagram. This data, in its oversimplified form, is called Big Data. To understand Big Data is to understand data in its most complex form, and this understanding helps organizations harness their data and use it to identify new opportunities. That leads to smarter business moves, more efficient procedures, higher profits and happier customers.
Data Scientist is one of the fastest growing jobs in recent years due to high business demand. Before diving into how and what you should look out for when hiring a Data Scientist, the first thing you should know is that no two Data Scientist roles are exactly alike. Unlike the roles of a lawyer or a doctor, for instance, Data Scientist is a relatively new and buzzword job title. As a result, there is a great degree of inconsistency between roles that have that title, and with that comes variability in the day-to-days of the professionals that have that role.
There are lots of alternative job titles that have a Data Scientist job description actually sitting behind them. They include:
- Machine Learning Engineer
- Business Intelligence Developer
- Analytics Consultant
- Research Scientist
- Data Engineer
- Artificial Intelligence Developer
- Marketing Analyst
- Risk/Fraud Analyst
There are also lots of questions in regards to the differences between data scientists, data analysts and data engineers are because all of them take aspects of each others.
We have already covered previously that we can classify Data Scientists as research-focused, business-focused, or development-focused. In general, these are the main hard and soft skills they all should have.
- Programming knowledge like Python, C++, Java and SQL;
- In-depth knowledge of machine learning and deep learning;
- Being familiar with Apache Spark, Apache Hive, and Apache Pig is desirable, along with the knowledge of Hadoop;
- Data visualization and business intelligence skills for creating reports and dashboards;
- Communicate and present information and ideas clearly.
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Beyond technical expertise above mentioned, there are also certain soft skills you should take into consideration when hiring a Data Scientist. He or she should possess the following soft skills and requirements:
Critical thinking - Data teams must be able to connect current dots to previous work and determine how previous methodologies may be applied to a new problem.
Problem-spotting - Data scientists are expected to be “problem solvers,” but data science teams, as a group, must help pinpoint issues across your organization. Otherwise, they will solely work on the problems and solutions using existing technologies, limiting their ability to push our business forward.
Knowing the stakeholders - For each project, data science teams must be able to identify the various stakeholders. They must also describe how a data analytics project will be carried out, and the outcomes presented, considering the many parties involved.
Listen and communicate - The impact of data science is increased by listening to stakeholders' demands and communicating properly with them. Otherwise, data teams are unlikely to achieve their full potential.
Adaptability, flexibility, patience, and perseverance - Data technologies, systems, and tools are constantly evolving, just as new platforms are being designed. As a result, human characteristics that aid in anticipating and adapting to these changes are essential. The capacity for a Data Scientist to learn and adapt to new circumstances becomes more important than having a thorough understanding of technology.
While technical competence is a must for hiring a team of scientists, it cannot be the primary requirement. Establishing and maintaining a high-value data science team demands a comprehensive, long-term strategy, hence you must ensure you’re hiring the right Data Scientist.
If you are looking to build a team on your own, the best places to find skilled Data Scientists teams include LinkedIn, Upwork, Glassdoor and DataCamp.
On the other hand, you might want to consider a different approach if you want a more robust strategy and staff extension may just be what you’re looking for. The major advantages of expanding the capacity of your team with specialized skills is being able to scale faster, cut on operating and hiring costs, have access to a wider pool of talent, higher flexibility to expand your team and access to the know-how of the service provider.
Imaginary Cloud also provides award-winning Artificial Intelligence and Data Science services and has taken businesses to the next level for more than a decade!
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