커리어특강자료_글로벌커리어 및 인공지능 커리어

해외 이직 및 성장 경험; 인공지능 분야 커리어에 대한 고찰
Image: used under license from shutterstock.com
29 Oct, 2020 카이스트 경영대학
김인준, 정보경영 석사과정(IMMS) 2013
Vice President, United Overseas Bank
[Contact]
blog: injunkim.com
Email: injune.kim@gmail.com

2
Bio – InJun Kim(김인준)
KAIST 정보경영 석사과정(IMMS) 2013, 최우수 논문상
현 United Overseas Bank, Singapore, Vice President, Group AI Strategy
전 Munich Re, Singapore, Lead Data Scientist, Data Science Service
전 IBM GBS(Watson), Hong Kong, Sr. Engagement Manger, Insurance AI
전 IBM GBS(Watson), Korea, Cognitive & Analytics, Managing Consultant
전 PwC Korea, Senior Consultant, Performance Improvement Consulting
‘인공지능 모델 편향 리스크 및 거버넌스 프레임워크’ 관련 강의 및 발표
• 성균관대학교 인공지능대학원, 전문가 초청 세미나 ‘인공지능 모델 편향 리스크’, 10월 ‘20년
• 카이스트 정보미디어 MBA, 정보경영 석사과정 등 특강 ‘인공지능 도입 전략’, 11월 ‘20년
• AI Summit 2020 in Seoul, 12월 ‘20년

I. 해외 이직 및 성장 경험
1. 해외 이직 경로 들 및 장단점
2. 나의 경로 및 Tips
3. 글로벌 커리어를 추구하게 된 이유
4. 성공이 아닌 성장
Topics Outline
3
II. AI 및 Data Science 커리어
1. 채용 트렌드(팬데믹 상황 하)
2. 직무에 대한 고찰
3. 진입 전략
4. 제언
III. Q&A

Topics Outline
4
3. 진입 전략
4. 제언
III. Q&A

5
경로 장점 단점
1.외국계 회사 서울 오피스 → 해외 오피스
Relocation
• 이직이면서 이직이 아님
• 적응이 빠름
• Relocation 옵션 중 선택
• 낮은 확률
• 평소에 쌓은 평판 > 인터뷰
2.목표 국가로 나감 → 현지에서 Job을
찾는 경우
• 비자 해결 후 지원, 대면 인터뷰 등 참여 가능 • (6개월)안에 Job을 찾지 못하면
떠나야함(나라, 학교의 랭킹에 따라
다름) ; 생활비 ; 불확실성
3.한국기업 → 해외 주재원 • Relocation(현지정착/자녀교육대한 경제적인
지원
• 현지사업의 운영관리 역할을 담당하게 됨에
따라 기존 현업업무보다 폭넓은 경험
• 업무 특성 상 몸은 해외에 있지만
한국에서 받는 스트레스들을
고스란히 받을 수 있음
4.한국에서 해외 포지션에 지원 → 오퍼
수락 후 해외로 나가는 경우
• 비자 등 모두 해결 된 후 출국
• 기회비용 적음 ; 여러개 나라 동시에 타겟팅
가능
• 새로운 문화 적응
• 오퍼수락에 필요한 정보 부족
• Low Risk, Low Return
5.(4번과 유사) 해외에 있는 한국법인
현지채용 (한국 기업의 현지 법인 근무자)
• 현지 채용 수요가 지속적으로
존재(마케팅/디자인/개발/인사재무 직무)
• 빠른 적응
• 현지문화와 한국문화의 공존
• 현지 상황을 잘 알지 못하는
헤드헌터
6.목표 국가에서 학위 이수 → 현지 취업 • 비자 스폰을 전제로 학교에 찾아오기에
4번보다는 유리
• 전략적인 인텁쉽 활용 가능
• 이전 경력, 언어능력 등에 따라 현지
취업에 실패 할 수 있음
• Seniority

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경로 Tip
1.외국계 회사 서울 오피스 → 해외 오피스
Relocation
2.목표 국가로 나감 → 현지에서 Job을 찾는 경우
3.한국기업 → 해외 주재원
4.한국에서 해외 포지션에 지원 → 오퍼 수락 후
해외로 나가는 경우
5.(4번과 유사) 해외에 있는 한국법인 현지채용
6.목표 국가에서 학위 이수 → 현지 취업
• 글로벌 회사에서 브랜드가치 향상하기
• 글로벌 회사에서 네트워킹 하기 (조직도, 내부 플랫폼 등 활용 )
• Relocation 기회 탐색
• Target Offices 리더들에게 연락하기
• 현 Office 매니저와 커뮤니케이션 하기; Relocation 옵션 중 선택하기
• (지원) 인사이트(예: 왜 해당 포지션이 오픈되었는지) 확보 후 Resume,
Email 등 작성하기
• (인터뷰) 평소 링크드인에 직무관련 전문성을 집약 시킨 후 → 링크드인
등을 통한 사전 네트워킹
• 전략적 헤드헌터 활용 - 의존하지 말 것
• 타겟 마켓은 신중하게 결정
• 나라/ 산업/ 갑을 3개 모두 바꾸는 것은 어려우면서도 높은 스트레스

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• IBM 그리고 인공지능(Watson)
• 한국기업의 문화, Work & Life Balance, 좋은 아빠 등 개인적 가치
• 한국은 나이, 경력년수, 출신 등이 상대적으로 성공의 속도에 높은 영향
• 연봉
• Regional and First-of-its-kind Project
• Regional Networking
• 주류가 없는 Melting Pot ; 다른 문화(비즈니스, 생활)
• 합리적 개인주의 ; 빠름에 대한 고찰 ; 역으로 한국의 장점이 객관적으로 보임
• 가족 친구와 떨어져 있어야 하는 것은 가장 큰 단점
글로벌커리어를추구한이유6년여해외커리어후

8
4. 성공이 아닌 ‘성장’
• 영어 < 문화
• From Scratch(내외부 네트워크, 리더십)
• 중기적인 계획이 없다면 더 외로운 시기가 될 수도
• 만약 내부 정치에 휘말렸다면 당신을 지켜줄 멘토는 없을지도
• PR등이 없다면 그 나라에서 이직하는 것도 굉장히 어려움(고용 안전성)
• 한국보다 나이/학벌 등은 문제가 되지 않지만, 또 반면 외국인을 승진 등에 우선
고려하지 않을 수도
• 특히 을이라면
o 끊임없이 토론해야 하며, 서로 경쟁해야 하는 상황도 상대적으로 많음 – 문화적 요소 작용
o 반면 고객에게 신뢰를 쌓음으로서 좋은 평판을 얻어 올라 가는 것은 컨설팅직무에서는 가능
(거대한 갑 회사에서 외국인 노동자로 일할때 대비)

Topics Outline
9
3. 진입 전략
4. 제언
III. Q&A

10
1. AI 및 Data Science 채용 트렌드 - 글로벌
the number of AI jobs globally could
increase by as much as 16% this year,
reaching 969,000, driven by stronger demand for
AI workers as companies contend with the impact of the
pandemic.
Before the outbreak, IDC estimated gains of
13.3% for 2020. The firm’s worst-case
scenario is 11% growth. The range reflects the
uncertainties around the full impact of the coronavirus
outbreak later in the year, said Ritu Jyoti, global AI
research lead at IDC. IDC’s forecast includes a
variety of AI-related jobs, such as data
engineers, data scientists and machine
learning developers.
Job openings for AI-related jobs have
seen a 28% increase from August 2019
to August 2020, while job searches have
seen a 91% spike.
Sources: Technology research firm International Data Corp.
Sources: bwpeople.businessworld.in/
Listings for AI roles dropped to only
4.6% year-over-year compared with 14%
prior to the outbreak..
“AI specialist hiring is slowing … but
that doesn’t mean that AI adoption is
slowing down,”
Sources: venturebeat.com

11
1. AI 및 Data Science 채용 트렌드 - 국내
Sources: 인쿠르트

12
2. AI 및 Data Science 직무에 대한 고찰 – AI Product 유형 별
INSIGHT
PROCESS
PRODUCT
Embed AI capabilities in
product or service to
provide customer benefits,
such as easy of use,
simplicity
Embed AI capabilities into organization’s workflow
(e.g. automating tasks, things done faster/better/cheaper)
Use AI capabilities to
uncover insights to make
better operational and
strategic decisions

13
2. AI 및 Data Science 직무에 대한 고찰 – Lifecycle 별
Machine Learning Engineer
Data Scientist
Data Engineer
AI Architect
AI Translator
AI Engagement Manager
AI Product Manager
AI Ethicist
UX Designer
Ideation Data Sourcing
Model Development
and Evaluation
Industrialization, Monitoring,
and Maintenance
AI Product/Solution Lifecycle

14
2. AI 및 Data Science 직무에 대한 고찰 – Upskilling에 의한 진입
Data Scientist
Data Engineer
AI Consultant
Machine Learning Engineer
AI Ethicist / Model Risk Manager
AI Projects/Product Manager
Programmer / Developer
DB Admin, SQL Coder
Strategy/IT Consultant
App Developer/System Engineer
Risk and Compliance Experts
Projects/Product Manager

TrendingSteady
Business Technical
15
2. AI 및 Data Science 직무에 대한 고찰 – 2x2 분류
Machine Learning
Engineer
Data Scientist
Data Engineer
AI Architect
AI Translator
AI Engagement Manager
AI Projects/Products
Manager
AI Ethicist / Model Risk
Manager
UX Designer
Change Management and
AI Re/UpSkill Expert
AI Consultant

16
2. AI 및 Data Science 직무에 대한 고찰 – Trending Jobs
AI Ethicists
• AI ethicist (IBM)
• Chief Ethical and Humane Use Officer
(Salesforce)
• Data Privacy and Ethics Lead (Qantas)
• Emerging Risk Analyst – Trust & Safety
(TikTok)
• Ethical AI Lead (Google)
• Chief AI Ethics Officer (US Army AI Task
Force)
• Activation Lead / Ethics & Society
(Microsoft)
• Director of Responsible Innovation
(Facebook)
• Social Trust Specialist (Apple)
• Responsible AI Specialist (H&M Group)
• Staff Product Manager – ML Ethics,
Transparency and Accountability (Twitter)
• Principal, Responsible Innovation + Data
Ethics (Accenture)

17
• Data Engineer works on the databases to ensure that all the sources
are available and to provide curated datasets to the rest of the
analytics team.
• Data Scientists explore the data, train, and build the predictive model.
They then work with the ML Ops Engineer to test the trained model on
production data in a development or “sandbox” environment, after
which the engineer takes that model and deploys it into production
environments (e.g., first into pre-production, then into final production).
• Data Scientists and ML Ops Engineers both incorporate
requirements and changes from business users to understand what the
model is meant to predict and what regulatory constraints are
applicable.
• Application designer works with the Data Scientists to decide which
models to use or build and how best to integrate the model so that it is
useful to business users, and the application developer writes the code
to weave the model into the application.
• DevOps team smooths the transition from development to
IT Operations, which maintains the infrastructure and ensures that the
application and its models run, perform, and scale optimally.
ML (Ops) Engineer

18
ML (Ops) Engineer
• With one foot in data science and one foot in DevOps,
the ML Ops engineer plays the newest role in the pipeline
and an important one in the successful operationalization of
ML models.
• The role bridges the aforementioned division of labor
between data scientists and the people responsible for
deploying, maintaining, and monitoring applications that
use predictive models.
• With the data scientist, the ML Ops engineer tests and
deploys models and sometimes implements monitoring.
The role involves a combination of understanding ML
models and data transformations, plus engineering
(usually at the level of scripting).

19
3. 진입 전략 – Business Track : AI Architect
The AI Architect is responsible for the definition, design and build of solutions that drive business growth
and productivity through automation. The role will focus on emerging AI / Automation technologies such as
Digital Assistants (chatbots), Machine Learning, and Robotics Process Automation. The Architect works
closely with all team members providing subject matter expertise and solutions to the most challenging
software development tasks.
Responsibilities
•Creative translation of business needs into a conceptual, logical and physical architecture design of the
solution
•Collaboration with the technical teams needed to deliver the design
•Leading architecture and design competency for the automation technology stack
•Strategic direction for maximizing simplification and re-use lowering overall TCO
•Creation of solutions which adhere to Enterprise Architecture, Development, and Information Risk
Management standards and policies
•Collaboration with developers and engineering teams resolving the most challenging tasks ensuring the
proposed design is properly implemented.
•Proactively identify and address technical strengths, weaknesses, opportunities and threats across the
automation landscape
•Strategy for managing the changes to the architecture (new business needs, technology changes, etc.)
•Explain architecture standards and recommendation to business partners and drive alignment
Key requirements
•Minimum 5 years of hands-on experience in the IT field, at least 3 years in IT architecture
•Solid experience as solution and platform architect, providing technical leadership and guidance designing
enterprise software.
•Proven track record at managing multiple projects in fast paced cross-functional team environment.
•1+ year year experience working with chatbots is a plus
•2+ years hands-on experience with Microsoft Azure and Amazon Web Services
•Working knowledge of Machine Learning, techniques, algorithms, natural language processing
•2+ years of development experience (.NET, Python are ideal)
•Certifications in multiple cloud platforms is ideal
•2+ years of experience working in an Agile Scrum / DevOps development methodology.
•Bachelor’s degree in Computer Science or related field is required.

20
3. 진입 전략 – Business Track : AI Translator
An AI Translator that will bridge the technical expertise of data scientists with the operational
expertise of business teams to help us make smarter business decisions to deliver even better products
and experiences to our customers. ..
Responsibilities
•Develop relationships with business teams to help them understand the value of AI/analytics and
organically build the customer analytics demand pipeline
•Develop measurement strategies that are used to drive the visual management of the business teams’
goals and operating reviews.
•Help business leaders craft questions to generate maximum business value using analytics
•Help data scientists/business teams write those questions into user stories; translate business questions
and goals to data scientists and help them design the analytics solutions
•Translate the analytics solutions back to the business to ensure its actionable and business can drive
value from those insights
•Help track benefits of these insights to leadership to drive adoption; manage analytics scorecard and
help it become a leadership tool to drive value
•Become a customer data Subject Matter Expert to help expedite analytics problem solving
•Become Product Owner of Customer Analytics Scrum team to help with analytics user story prioritization
and build prioritization framework
•Document data sources, data elements and data architecture
Skills and Qualifications
•Bachelor’s degree in the field of business, IT, or data science. Advanced degree is a plus.
•7+ years overall experience required
•2+ years of demonstrable analytics experience
•Strong business acumen
•Strong communication and storytelling skills
•Self-starter and intellectually curious
•Comfortable in pushing the conventional thinking to drive change and innovation
•Past experience as business analyst, scrum master, project manager and/or data analyst preferred
•Passion for data and analytics
•Work well with technical and management teams
•Ability to translate analysis and conclusions into actionable recommendations
•Comfortable with metrics, measurements

21
3. 진입 전략 – Technical Track : Data Scientist
Sources: https://www.addo.ai/

22
3. 진입 전략 – Technical Track : Data Scientist
Sources: mrdbourke.com

23
Recommended Resources
AI Product Manager - Vendor AI Strategy Data Management Trend
AI Ethics: Responsible ML
Amazon Science The 10 fairness-in-AI projects funded by the National Science
Foundation in collaboration with Amazon
https://responsible-ai.devpost.com/project-gallery
ML Ops
Agile + Data Science

24
4. 제언 - MBA 후 AI/Data Science 직무를 고려하고 있다면
• Data or AI?
• 갑 or 을?
o 갑: 어떤 산업?
o 을: 컨설팅회사? 스타트업?
• 한국 or 해외?
• 얼마나 많은 학문적 지식 그리고 학위가 필요?
• AI-led Hyper Personalization 등 서비스/상품팀과 함께 일하는 직무 역시 추천

Topics Outline
25
3. 진입 전략
4. 제언
III. Q&A

Thank you.
Image: used under license from shutterstock.com
김인준, 정보경영 석사과정(IMMS) 2013
Vice President, United Overseas Bank
[Contact]
blog: injunkim.com
Email: injune.kim@gmail.com

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