Tips for Building a Data Science Capability

Booz Allen identified key activities, tasks,
and attributes required to successfully
perform in a data science role to help
clients understand the work to be
performed.
WHY THE JOB ANALYSIS MATTERS:
+ Helps clients define and understand the
most important work of their data
science teams
+ Confirms the importance, frequency,
and relevance of tasks and knowledge,
skills, and abilities (KSAs)
+ Assists organizations to address
expansion of mission, establishment
of a new role, or an influx of new staff
+ Establishes the legal and scientific
basis
Booz Allen developed a scientifically valid
competency framework to help clients
determine what KSAs their data science
team needs.
WHY THE COMPETENCY FRAMEWORK
MATTERS:
+ Provides clients with insights into what
characteristics and KSAs are required
for a data scientist’s individual success
+ Establishes a legally defensible
foundation for human capital practices
+ Useful when hiring/selecting talent,
developing the workforce, assessing
training needs, etc.
Data scientists have a unique blend of
knowledge, skills, behavioral attributes,
and personality traits that enable them to
combine and analyze massive amounts of
structured and unstructured data to solve
some of an organization’s most complex
problems. Their inquisitive nature, as well
as their ability to implement the scientific
method and advanced tools and techniques,
makes them exceptionally unique
and vitally important in today’s competitive
environment. Yet few organizations have
taken a proactive, rigorous approach to
defining the unique characteristics of data
science professionals, systematically
documenting the type of work they perform;
the competencies required for success; and
the comprehensive approach to talent
management required to identify, attract,
grow, and retain these professionals.
Booz Allen Hamilton, a leading strategy and
technology consulting firm, accepted the
challenge. Recognizing the need to better
understand the key elements that make up
the data science role and data science
BOOZ ALLEN’S DATA SCIENCE COMPETENCY FRAMEWORK
Clusters
Technical: “Knows How and
What to Do”
Competencies
talent requirements, Booz Allen took a
rigorous, scientifically based approach to
designing the foundation of a data science
Talent Management Model. To meet the
needs of business leaders, human
resources professionals, and our own data
science workforce, Booz Allen started from
scratch, leveraging the expertise of our
human capital team and the knowledge,
capabilities, and experience of our 500+
data science team to describe core
elements of data science work activities;
define those characteristics that best
represent successful execution of the work;
and develop a Talent Management Model
that will arm clients with the insights
needed to best identify, attract, grow, and
retain data science talent.
DEFINING THE CORE ELEMENTS OF
THE JOB: JOB ANALYSIS
First, Booz Allen began by conducting a
comprehensive job analysis on our own
data science cadre. With the largest
Statistical Modeling; Research Design; Data Mining and
Integration; Data Visualization; Computer Science; Database
Science; Machine Learning; Programming and Scripting;
Information Assurance; Mathematics; Operations Research
Data Science Consulting:
“Can Do in Client and
Customer Environment”
Domain Expertise; Business Acumen; Program Management;
Resource Allocation; Collaboration and Teamwork; Data Science
Consulting; Ethics and Integrity; Communications
Cognitive: “Able to Do or
Learn to Do”
Critical Thinking; Problem Solving; Inductive and Deductive
Reasoning
Personality: “Willing or
Motivated to Do”
Perseverance; Innovation and Creativity; Resilience and Hardiness;
Inquisitiveness; Adaptability/Flexibility; Ambiguity Tolerance; Detail
Orientation; Self-Confidence; Work Ethic
32 | THE BOOZ ALLEN DATA SCIENCE TALENT MANAGEMENT MODEL