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Tips for Building a Data Science Capability

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BOOZ ALLEN TALENT MANAGEMENT MODEL<br />

Who You Need<br />

Key Benefits:<br />

+ + Identify the data science talent that will<br />

generate the most value<br />

+ + Rapidly define data science talent needs<br />

+ + Select talent based on legally defensible<br />

and scientifically valid data<br />

Job Analysis identifies key content in terms of activities and tasks<br />

involved and attributes required to successfully per<strong>for</strong>m in a given job,<br />

role, or position<br />

Competency Framework encapsulates knowledge, skills and abilities<br />

<strong>for</strong> successful job per<strong>for</strong>mance<br />

Targeted Position Descriptions define and differentiate the primary<br />

job responsibilities <strong>for</strong> a specific role within the organization<br />

Hiring Criteria and Assessments identify candidates with the<br />

essential ability, technical skills, and personality traits necessary <strong>for</strong><br />

successful job per<strong>for</strong>mance<br />

Where You Need Them<br />

Key Benefits:<br />

+ + Connect business goals to data science<br />

talent requirements<br />

+ + Shape the data science work<strong>for</strong>ce of<br />

the future<br />

+ + Mitigate talent risks be<strong>for</strong>e there is a<br />

business impact<br />

Work<strong>for</strong>ce Modeling defines the type and amount of work within an<br />

organization with respect to the available talent supply and demand<br />

Work<strong>for</strong>ce Design describes the composition of the work<strong>for</strong>ce and<br />

how work should be distributed across existing capabilities<br />

Work<strong>for</strong>ce Needs Assessment identifies and quantifies the need and/<br />

or prevalence of desired skills or competencies to be obtained<br />

How You Keep Them<br />

Key Benefits:<br />

+ + Reduce costly turnover in hard-to-find<br />

data science positions<br />

+ + Improve data scientists’ job satisfaction<br />

and engagement<br />

+ + Motivate and grow data science talent<br />

Career Paths/Matrix defines growth trajectories and transition points<br />

<strong>for</strong> personnel within the context of the organization’s business<br />

objectives<br />

Training and Course Development creates developmental learning<br />

opportunities to build new capabilities among junior talent and<br />

strengthen existing skill sets among senior talent<br />

Retention Strategies provide organizations with the reward structure<br />

and environmental factors to keep employees engaged and motivated<br />

Organization and Team <strong>Building</strong> helps clients further develop<br />

leadership skills and team dynamics to accelerate<br />

34 | THE BOOZ ALLEN DATA SCIENCE TALENT MANAGEMENT MODEL

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