Top Skills to Look for in a Data Scientist

Technical Skills

1. Programming and Coding Skills: A data scientist has to be proficient in different programming environments to be able to analyze data. R and Python are gaining traction to become the language(s) of choice in data science. Knowledge of SQL comes in handy to efficiently code complex queries and scripts. Familiarity with Hadoop is an added advantage.
Anand Rao, global artificial intelligence and innovation lead for data and analytics at consulting firm PwC, says “To be really successful as a data scientist, the programming skills need to comprise both computational aspects — dealing with large volumes of data, working with real-time data, cloud computing, unstructured data, as well as statistical aspects — [and] working with statistical models like regression, optimization, clustering, decision trees, random forests, etc.”
One can implement statistical knowledge, easily analyze large datasets and create tools using their coding skills. 

2. Machine Learning: If your organization deals with large amounts of data, an understanding of machine learning algorithms comes in handy for the data scientist. It is important to understand what techniques to apply in different situations. Celeste Fralick, the chief data scientist at security software company McAfee says, “Being aware of the computational cost to the ecosystem, interpret-ability, latency, bandwidth, and other system boundary conditions — as well as the maturity of the customer — itself helps the data scientist understand what technology to apply”.

3. Statistical Knowledge: For a data-driven approach, knowledge of statistics is valuable to the data scientist, as stakeholders will seek her help to make informed decisions and design or evaluate experiments. She should be familiar with statistical tests, distributions, maximum likelihood estimators, etc. It is important for data-scientist to have the statistical sensibility to know which test to run when and how to interpret the results.

4. Data Wrangling: A data scientist should be able to deal with unstructured data coming from different sources. Inconsistent formats, missing values or data duplication are some of the problems she may have to deal with when handling raw data. A data scientist should be able to cleanse the data and structure it to bring it under one roof to be able to gather information from it. 

Analytical Skills   

1. Quantitative and Qualitative Analysis: being able to find meaning out of the humongous amount of data an organization produces is critical. She should be able to handle data processing and create useful models based on the business context. Data Scientists can help their teams understand how the complex business environment works as well as design experiments to test various hypotheses and model systems to quantify/ predict which growth avenues are most valuable for the company.  

2. Critical Thinking: Ability to analyze a situation objectively from various perspectives, taking into account risk factors and vulnerabilities of each outcome before rendering conclusions, is a key skill for a data scientist.

3. Data Visualization: Data Visualization can aid in quick decision-making by representing findings in a form that users can relate to. Data Visualization skills help uncover hidden patterns and trends which are not obvious at the surface and may not even be apparent when presented in tables and calculations. A good data scientist must master data-visualization tools and be knowledgeable of principles behind visually encoding data and information communication.

Soft Skills

Apart from technical and analytical skills, what distinguishes a good data scientist from a great one are certain soft skills which include:

1. Communication Skills: Data scientists need to work with multiple functional leaders. Being able to present insights and interesting patterns in a clear and concise manner to business executives is an art a data scientist must master so that they can use this information effectively. 
Also, a data scientist has to interact with the team of engineers, designers, managers and decision makers. Good communication skills come in handy to facilitate understanding and building trust. 

2. Teamwork: Since, a data scientist needs to interact with many personnel at various levels, being a good team player who shares knowledge, encourages feedback and prioritizes business goals can become a valuable asset for the team.

3. Intellectual Curiosity: Being curious is an inherent skill what most data-scientists posses. The constant urge to discover insights, explore data in creative ways, being inquisitive to look for interesting patterns and taking initiatives to ask questions and solve business problems make for an expert data scientist.

4. Business Acumen: A comprehensive understanding of business goals and industry will help a data scientist identify and prioritize business problems. She should be able to discern critical problems, business challenges, and identify creative ways to leverage data to make important decisions.