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Ultimate Job Toolkit for Data Scientists

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Data Specialists and Data Scientists are 2 of today’s hottest job titles.  Number loving, analytical, left-brainers thrive in these positions, so if this sounds like you, listen up! For this particular Toolkit, we interviewed a number of 24 Seven recruiters to get a full understanding of this elusive job title. Today, we're sharing the exact skills, experience, and resources needed to flourish as a Data Scientist.

Data Scientist job description:

Data Science roles can include artificial intelligence, natural language processing, machine learning, deep learning, recommendations, and speech recognition (the list goes on). Data Science is used in all industries, but the specific job description will depend on the business need for this position. Data Scientists must tap into their built-in algorithmic and programming disposition. Some Data Scientists will be asked to build models to improve profitability, growth, retention, and other key performance indicators (KPIs) internally and externally (for clients), while others will develop innovative customer analytics, customer story strategy, and contact optimization tools. Creating practical methods to forecast demand, understand customer preferences, simulate business outcomes, and optimize the marketing investment are other potential responsibilities. Data Scientists must be able to explain complex modeling approaches in layman’s terms and discuss modeling results with non-technical business associates.

Skills Data Scientists need:

According to 24 Seven's Account Manager Anjelica Dumanovsky, Data Scientists must "be detail oriented and have the ability to focus in on tedious tasks" for long periods of time. Because of this, Dumanovsky recommends thinking "big picture, not just about plugging and chugging numbers." Candidates have to have a keen eye to "see how metrics affect the group cross-functionally." A Data Scientist's role is extremely number heavy which requires "exceptional knowledge of Excel", says Dumanovsky. Proficiency in Microsoft Office Suite, SQL, SAS, R, and/or Python may also be necessary.

How to get Data Scientist experience:

Data Scientists can acquire experience through education with a Bachelor’s degree in Mathematics, Business, Statistics, Economics or Computer Science. Dumanovsky also recommends taking "contract gigs anywhere you can." Roles such as "Sample Coordinators and Merchandise Assistants are great entry level positions that require Excel and data work" which will set candidates up for a future in data science.

What Hiring Managers look for:

Hiring Managers are on the lookout for candidates with "extensive experience within Excel, knowing how to maneuver freely in the program and utilize its shortcuts." Knowing "order management software skills, like Netsuite, are often sought after as most of the job will function in a program like this." Hiring Managers also look for someone who is "adaptable."  Dumanvosky explains, "sometimes the role can feel mundane and unimportant, but managers want to know you can still achieve the tasks even when feeling particularly unenthusiastic, again coming back to the point of being able to see the big picture."

Key components to success:

The key components to success as a Data Strategist include "doing research on new data entry software and educating yourself on shortcuts in your daily tools" according to Dumanovsky." Being able to show to management you can multi-task well and take on new projects" is key to climbing the ranks quickly.

Click here to access our free Job Toolkit for everything you need to know about today’s most in-demand jobs.

Additional resources to sharpen your skills:

Udemy Data Scientist course

SAS Academy for Data Science

Looking to test the waters in the data industry? See our relevant job openings today!