Data Scientist (Junior) – Data Science & Analytics

Occupational Standard

(for use in the development of Business Technology Management related job descriptions, performance evaluations, career development plans, educational learning outcomes etc.) Description of Position Data Scientists are responsible for modeling complex Institute

problems, discovering insights and identifying opportunities through the use of statistical, algorithmic, mining and

visualization techniques. In addition to advanced analytic skills, this role is also proficient at integrating and preparing large, varied datasets, architecting specialized database and computing environments, and communicating results.

In most organizations, Data Scientists work closely with clients, data stewards, project/program managers, and other IT teams to turn data into critical information and knowledge that can be used to make sound organizational decisions. Other responsibilities include providing data that is congruent and reliable. They need to be creative thinkers and propose innovative ways to look at problems by using data mining (the process of discovering new patterns from large datasets) approaches on the set of information available. They will need to validate their findings using an experimental and iterative approach. Also, Data Scientists will need to be able to present back their findings to the business or organization by exposing their assumptions and validation work in a way that can be easily understood by their business

counterparts. These professionals will need a combination of business focus, strong analytical and problem solving skills and programming knowledge to be able to quickly cycle hypothesis through the discovery phase of the project. Excellent written and communications skills to report back the findings in a clear, structured manner are required.

Position Development Advancement to manager level positions is possible through progressively responsible leadership positions and management experience. The career path will be determined by the size, type, geographic scope, culture, and organizational structure of the firm offering employment.

Required Qualifications

Education Post-secondary education is preferred, usually a Bachelor’s degree in a business, computing or engineering field. Follow up technical educational may also be required depending on the technologies in use at the various organizations. Moreover, many organizations require senior Data Scientists to complete post-secondary school in any of the following areas: mathematics, statistics, economics, computer science, commerce, or

115 skills, knowledge, work-related experience, and/or industry courses and programs. Some organizations will send individuals to specific enterprise solutions training courses and programs to learn additional tools and techniques.

Related Work Experience Individuals may have experience in any of the methodologies and techniques used as a junior data scientist. Often this experience may be augmented by specific industry experience using industry or use case specific tools (e.g. R, SAS, python, etc.). Data

Scientists (junior) may also require several years of experience in data analysis, modelling, business requirement specification, qualification and assurance, systems analysis, data

administration, software engineering, as well as project management and supervisory experience. Typically, data scientists require experience manipulating large datasets and using databases, as well experience with a general-purpose programming language (such as Hardtop MapReduce or other big data frameworks, or Java). Data scientists also typically have experience using statistical packages and have familiarity with basic principles of distributed computing and/or distributed databases.

Tasks  Designs experiments, test hypotheses, and build models.

 Conducts data analysis and designs algorithms

 Applies basic statistical and predictive modeling techniques to build, maintain, and improve on multiple real-time decision systems

 Leads discovery processes with key stakeholders to identify business requirements and expected outcomes.

 Works with and alongside more senior data scientists and statisticians to build robust models

 Models and frames business scenarios that are meaningful and which impact on critical business processes and/or decisions.

 Identifies what data is available and relevant, including internal and external data sources, leveraging new data collection processes such as smart meters and geo-location information or social media.

 Collaborates with subject matter experts to select the relevant sources of information for new, tough problems

 Makes strategic recommendations on data collection, integration and retention requirements incorporating business requirements and knowledge of best practices.

 Validates analysis using scenario modeling

 Defines the validity of the information, how long the information is meaningful, and what other information it is related to.

 Works with internal data stewards to ensure that the information used is in compliance with regulatory and security policies.

 Qualifies where information can be stored or what information, external to the organization, may be used in support of the use case.

 Develops usage and access control policies and systems in collaboration with the data steward.

 Partners with the data stewards in continuous

improvement processes impacting data quality in the context of the specific use case.

 Recommends on-going improvements to methods and algorithms that lead to findings, including new

information

 Presents and depicts the rationale of their findings in easy to understand terms for relevant stakeholders

 Educates their organization both from IT and the business perspectives on new approaches, such as testing

hypotheses and statistical validation of results.

 Helps the organization understand the principles and the math behind the process to drive organizational buy-in.

 Provides business metrics for the overall project to show improvements (contribution to the improvement should be monitored initially and over multiple iterations).

 Demonstrates clarity, accuracy, precision, relevance, depth, breadth, logic, significance, and fairness

 Leads the design and deployment of enhancements and fixes to systems as needed.

Tools and Technology  Statistical analysis software

 Data analytics or intelligence programs

 Office productivity tools

 Software development tools and dev. ops tools including language specific IDE’s, GIT, etc.

Required Competencies Knowledge

Data Scientists should have knowledge of:

 Large complex data analytics or intelligence programs

 Data, statistics, and big data concepts that relate to data analysis

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 Various architectures including distributed architectures

 Software development methodologies relating to analysis

 Architectural understanding of the data and big data ecosystem

 Best practices in data delivery and measurement for the individual organizations that they work for or with

 Policies and principles for the management of information

 Relevant information standards and their appropriate use

 Basic technologies and workflow for the purposes of analysis, design, development and implementation of information systems and applications.

 Organizational or industry specific terminology and commonly used abbreviations and acronyms

 Commonly used formats, structures and methods for

recording and communicating data, as well as knowledge for how this data is incorporated for system and application use.

 Architectural relationships between key information technology components and best practices in enterprise architecture frameworks/perspectives.

 Appropriate informatics standards and enterprise models to enable system interoperability (e.g., terminology, data structure, system to system communication, privacy, security, safety).

 Key information technology concepts and components (e.g., networks, storage devices, operating systems, information retrieval, data warehousing, applications, firewalls, etc.).

 The ability to identify relevant sources of data needed to assess the quality of information & draw appropriate conclusions

 Statistical & analytical tools, techniques and concepts

 The ability to present data and information in a way that is effective for users and consumers of the data

 Knowledge of the indicators and metrics important for the specific business that they are measuring

Skills Data Scientists should have skills in the following categories:

Technical

 Demonstrable knowledge and experience of large, complex data analytics or intelligence programs

 Statistical, pattern recognition skills

 Understanding of data concepts

 Understanding of data technology and tools

 Experimental design, set-up, and modelling

 Experience with applicable analytics platforms, tools and technologies

 Architectural understanding of the data and big data ecosystems

Contextual

 Full understanding of the organization and of its

requirements and opportunities in data/big data analytics

 Experience in targeting tradecraft as well as experience in cargo screening, person screening, operational targeting

 Experience managing a team and working with senior level Government clients on consulting projects

 Strategic thinking

Personal Attributes A Data Scientist should have the following personal attributes:

 Communication skills

 Presentation and public speaking skills

 Rapport building and networking

 Innovation and creativity

 Leadership skills including ability to influence others, to lead business and 
technology programs, projects, workshops and initiatives, to inspire confidence 
and garner respect from business and technology stakeholders

 Planning, supervision, coaching and delegation skills

 Decision making skills

 Negotiating skills

 Research skills

Abilities A Data Scientist should have the following abilities:

 Ability to explain complex concepts to lay person

 Ability to collaborate with multiple skills and cross-functional expertise.

 Ability to communicate the benefits of analytical approaches simply and clearly

 Ability to communicate with top executives, business management, IT management, solution architects, technical architects, subject matter experts, partners and 
customers.

 Ability to adapt vocabulary and style for each situation


 Ability to present appropriately to a variety of audiences, including large audiences, top executives, business and

119 business and technology problems

 Ability to seek standardized solutions for problems where available

 Ability to find solutions across a wide range of

technologies and business domains. Often solutions have budget, time or operational constraints

Work Values Individuals who are effective as Data Scientists are:

 Able to communicate at all levels of organization

 Able to present complex ideas with simple visuals

 Able to find solutions across a wide range of technologies and business domains

 Able to facilitate collaboration

 Enjoy problem-solving

 Highly analytical

 Able to work independently

Work Styles Data Scientists would have the following work styles:

 Collaborative

 Cooperative

 Stress tolerant

 Initiative

 Independent

 Integrity

Essential Skills Profile A Data Scientist would have the following essential skills profile:

 Reading text

 Document use

 Writing skills

 Numeracy

 Oral Communication

 Thinking Skills

 Problem Solving

 Decision Making

 Job Task Planning and Organizing

 Significant Use of Memory

 Finding Information

 Working with Others

 Continuous Learning

Additional Information

Physical Aspects Data Scientists work extensively in an office environment (sitting for long periods, repetitive computer and telephone use).

However, Data Scientists may also be required to travel to satisfy the position function. Typically there is no heavy lifting,

bending, or stooping required; however, this is determined by the needs of the organization.

Attitudes Data Scientists should have very advanced interpersonal skills – be persuasive, empathetic, able to handle pressure, creative, have a sense of urgency, and attention to detail. Enterprise Data Architects must exhibit leadership, people management skills, advanced negotiation skills, advanced conflict resolution skills, and organizational and planning abilities. Adaptability and flexibility are important, as Data Scientists work with diverse multicultural workforces.

Future Trends Affecting

Essential Skills The ability to speak more than one language, and an awareness of and sensitivity to the diversity of international cultures is

considered a growing need in the face of increasing globalization.

Furthermore, familiarity with opportunities and benefits associated with “green IT” (e.g. server energy efficiency, reducing overall power consumption from IT related activities, etc.) will be of increasing importance as facilities begin to

manage their overall environmental footprint while seeking short and long term cost saving opportunities. A strong understanding of cloud computing will also serve all individuals in this position very well.

121 Occupational Standard

(for use in the development of Business Technology Management related job

descriptions, performance evaluations, career development plans, educational learning outcomes etc.)

Description of Position Enterprise data architects apply architecture principles and practices to IT and business problems in order to guide organizations through the business, information, process, and technology changes necessary to execute their

strategies. Enterprise data architecture involves enterprise analysis, design, planning, and implementation, using a holistic approach at all times, for the successful

development and execution of strategy. These practices utilize the various aspects of an enterprise to identify, motivate, and achieve these changes. An Enterprise Data Architect is a person responsible for performing this complex analysis of business or technology structure and processes with the goal of drawing conclusions from the information collected so that a solution can be developed.

They also create schematic documents used to solve problems and communicate those documents widely throughout their organizations.

Position Development Advancement to management level positions is possible through progressively responsible leadership positions and management experience. The career path will be

determined by the size, type, geographic scope, culture, and organizational structure of the firm offering employment.

Required Qualifications

Education Post-secondary education is preferred, usually a Bachelor’s degree in a business, computing or engineering field. Follow up technical educational may also be required depending on the technologies in use at the various organizations.

Training Enterprise Data Architects require on-the-job training;

however, typically organizations require that the individual will already have the required skills, knowledge, work-related experience, and/or industry courses and programs.

Some organizations will send individuals to specific

enterprise solutions training courses and programs to learn additional tools and techniques.

Related Work Experience Individuals may have experience in any of the

methodologies and techniques used as an Enterprise Data Architect. Often this experience may be augmented by specific industry experience using industry or use case specific tools (e.g. Cloud data tools).

Tasks  Communicate the benefits of various architectural approaches or designs to both business and engineering audiences

 Present solutions to a variety of audiences, including large audiences, top executives, business and

technical leaders

 Seek and find solutions to a wide range of business and technology problems

 Seek standardized solutions for problems where available

 Find solutions across a wide range of technologies and business domains

Tools and Technology  Office productivity tools

 Architecture diagram tools

 Software development tools and dev. ops tools including language specific IDE’s, GIT, etc.

Required Competencies

Knowledge Enterprise Data Architects should have knowledge of:

 The organization, structure, and relationship between the various systems existing within an organization as well as the organization’s overall structure and function

 Architectural relationships between key information technology components and best practices in Enterprise Data Architecture frameworks/perspectives for the specific businesses that they are working in

 Familiarity with technology frameworks that are relevant for their various industries

 Hardware, software, application and systems engineering best practices and goals

 Relevant organizational concepts, processes, technologies and workflow for purposes of

analysis, design, development and implementation of a data science & analytics driven information system

 Basic organizational terminology as well as commonly used abbreviations and acronyms

 Commonly used formats, structures and methods for recording and communicating data within a specific organization, as well as an understanding

123

 Appropriate informatics standards and enterprise models which enable system interoperability (e.g., terminology, data structure, system to system communication, privacy, security, safety)

 Project and program management planning and organizational skills

 Financial modeling as it pertains to IT investment

 IT governance and operations

 Policies and principles for the management of analytics data and information

 Data, information and workflow models that can be used to model information technology solutions

 Key information technology concepts and components (e.g., networks, storage devices, operating systems, information retrieval, data warehousing, applications, firewalls, etc.)

 The ability to identify relevant sources of data and information to assess quality of information and draw appropriate conclusions

 Appropriate analytical and evaluation techniques and concepts

 Knowledge on the best practices for visualizing and presentation data and information that is effective for users

 Knowledge of indicators and metrics for

organizational delivery & systems management Skills An Enterprise Data Architect should have skills in the

following categories:

Technical

 The ability to understand the big picture within an organization and the relationship between domains and components within it

 Systems thinking - the ability to see how parts interact with the whole (big picture thinking)

 Comprehensive knowledge of hardware, software, application, and systems engineering

 Project and program management planning and organizational skills

 Knowledge of financial modeling as it pertains to IT investment

 Ability to adopt a successful customer service orientation that applies to various stakeholders

 Time management and prioritization skills

 Systems & engineering thinking

 Emotional intelligence

Contextual

 Understanding of the business for which the

Enterprise Data Architecture is being developed (see above regarding various health care organizations)

 Knowledge of IT governance and operations Personal Attributes An Enterprise Data Architect should have the following

personal attributes:

 Communication skills

 Presentation and public speaking skills

 Rapport building and networking

 Innovation and creativity

 Leadership skills including ability to influence others, to lead business and 
technology programs, projects, workshops and initiatives, to inspire confidence 
and garner respect from business and technology stakeholders

 Planning, supervision, coaching and delegation skills

 Decision making skills

 Negotiating skills

 Research skills

Abilities An Enterprise Data Architect should have the following abilities:

 Ability to communicate the benefits of architectural approaches simply and clearly

 Ability to communicate with top executives, business management, IT 
management, solution architects, technical architects, subject matter experts, partners and 
customers.

 Ability to adapt vocabulary and style for each situation


 Ability to present appropriately to a variety of audiences, including large 
audiences, top executives, business and technical leaders

125

 Ability to seek standardized solutions for problems where available

 Ability to find solutions across a wide range of technologies and business domains. Often solutions have budget, time or operational constraints.

Work Values Individuals who are effective as Enterprise Data Architects are:

 Able to communicate at all levels of organization

 Able to present complex ideas with simple visuals

 Able to find solutions across a wide range of technologies and business domains

 Able to facilitate collaboration

 Enjoy problem-solving

 Highly analytical

 Able to work independently

Work Styles An Enterprise Data Architect would have the following work styles:

 Collaborative

 Cooperative

 Stress tolerant

 Initiative

 Independent

 Integrity

Essential Skills Profile An Enterprise Data Architect would have the following essential skills profile:

 Reading text

 Document use

 Writing skills

 Numeracy

 Oral Communication

 Thinking Skills

 Problem Solving

 Decision Making

 Job Task Planning and Organizing

 Significant Use of Memory

 Finding Information

 Working with Others

 Continuous Learning Additional Information

Physical Aspects Enterprise Data Architects work extensively in an office environment (sitting for long periods, repetitive computer and telephone use). However, Enterprise Data Architects may also be required to travel to satisfy the position function. Typically there is no heavy lifting, bending, or stooping required; however, this is determined by the needs of the organization.

Attitudes Enterprise Data Architects should have very advanced interpersonal skills – be persuasive, empathetic, able to handle pressure, creative, have a sense of urgency, and attention to detail. Enterprise Data Architects must exhibit leadership, people management skills, advanced negotiation skills, advanced conflict resolution skills, and organizational and planning abilities. Adaptability and flexibility are

important, as Enterprise Data Architects work with diverse multicultural workforces.

Future Trends Affecting

Essential Skills The ability to speak more than one language, and an

awareness of and sensitivity to the diversity of international cultures is considered a growing need in the face of

increasing globalization. Furthermore, familiarity with opportunities and benefits associated with “green IT” (e.g.

server energy efficiency, reducing overall power consumption from IT related activities, etc.) will be of increasing importance as facilities begin to manage their overall environmental footprint while seeking short and long term cost saving opportunities. A strong understanding of cloud computing will also serve all individuals in this

server energy efficiency, reducing overall power consumption from IT related activities, etc.) will be of increasing importance as facilities begin to manage their overall environmental footprint while seeking short and long term cost saving opportunities. A strong understanding of cloud computing will also serve all individuals in this

In document BTM Learning Outcomes and Competency Standards. Data Analytics Specialization Version 1.0. data analytics. Data Analytics Specialization (Page 114-137)