Career or Job Opportunities as Machine Learning Manager

스포트라이트

유사한 제목

AI Manager, Data Science Manager, Analytics Manager, Artificial Intelligence Manager, Data Engineering Manager

직무 설명

Ever since computers were first created, programmers have wanted them to be able to think for themselves. In fact, there’s an entire field of data science called machine learning dedicated to that goal!

As IBM explains, “Machine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy.”

Once confined to the realm of science fiction, today thousands of companies are heavily invested in AI and machine learning—with dedicated teams working hard to develop the technology further. These teams require the focused leadership of experienced Machine Learning Managers who understand their companies’ business objectives and know how to coach teams to succeed.

커리어의 보람 있는 측면

Working with teams on the cutting edge of technology
Developing programs to boost efficiency and meet business goals
Competitive compensation and great opportunities for skills development

내부 특종

직무 책임

근무 일정

Machine Learning Managers work full-time jobs, typically with nights, weekends, and holidays off, though overtime may be occasionally needed.

일반적인 업무

Look for areas where machine learning (ML) can be applied to existing projects and processes
Meet with company leadership and teams to explain concepts, propose strategies, and review potential impacts and benefits
Create a machine learning roadmap listing processes and problems, as well as the math, resources, and tools to be used
Implement ML initiatives according to the schedule
Lead ML teams which may include data scientists, engineers, and programmers
Boost user awareness of how ML is being adopted and what changes they may need to know about
Work with mobile device management teams as needed to ensure new data strategies are implemented efficiently
Generate and deploy algorithms capable of extracting useful information from large data sets
Objectively assess different methodologies and their results
Use programming languages and tools like Python, R, and TensorFlow
Develop automated processes for predictive model validation

추가 책임

Work with partner businesses as directed to share knowledge, insights, or information about changes
Build strong external partnership networks to enhance learning
Train or mentor team members and assistant managers

업무에 필요한 기술

소프트 스킬

분석
Business acumen
커뮤니케이션 기술
결정적
세부 사항 중심
Ethical
Independent
Leadership skills
Objective
Organized
Patient
문제 해결
팀워크

기술 역량

다양한 유형의 조직

Consulting firms
E-commerce/retail stores
Financial sector
정부 기관
Healthcare and pharmaceutical companies
제조
연구 기관
기술 기업

기대와 희생

Machine Learning Managers are expected to be at the peak of their game, and ready to effectively lead teams to meet ML-related organizational goals.

They must be creative, ethical, and forward-thinking, able to find and exploit all opportunities to integrate and leverage ML capabilities and boost performance. In this era of high-tech competitiveness, companies that fail to stay on top of trends may quickly fall behind and lose customers.

현재 트렌드

Machine learning is evolving rapidly and there are several notable trends to keep track of. Among them is the advancement of deep learning and deep neural networks inspired by the interconnected network of neurons in the human brain. Reinforcement learning is also a hot trend in robotics, training programs (aka agents) to interact with environments via trial and error.

As ML models become more complex, researchers must pay attention to ethical considerations and how ML models make decisions. Other trends include concepts like federated learning, transfer learning and pre-trained models, AutoML, edge computing, and on-device ML—each of which Machine Learning Managers need to learn about to stay up-to-date!

이 직업에 종사하는 사람들은 어렸을 때 어떤 일을 즐겼나요?

Machine Learning Managers were probably in love with technology at an early age. They may have been interested in math, computer coding, and programming languages. They likely also enjoyed analytical problem-solving or even reading about the impacts of technology on businesses.

Teamwork is an important part of this career field, but Machine Learning Managers are leaders who must be willing to act when there is disagreement. It’s their job to ensure appropriate ML behavior and decision-making. This ability to lead could have developed through extracurricular activities at school.

필요한 교육 및 훈련

Machine Learning Managers generally need a master’s degree in data or computer science or a related field
Workers do not start out as managers. Managers require several years of relevant work experience, including at least a few years of supervisory experience
Many managers are promoted from within the organization, working their way up from entry- or mid-level positions as ML engineers, programmers, or in some cases even business roles
Common course topics include:

Data modeling
Deep learning
Machine Learning algorithms and techniques
Natural language processing
Neural networks
Programming languages (R, Python, C++, Java) and Python libraries like NumPy, Pandas, Matplotlib, and Scikit-learn
Reinforcement learning
Relation between AI and ML
Statistics and probability

Students can learn programming languages like Python on their own, too!
Check out courses offerings from Coursera, such as its Artificial Intelligence: an Overview Specialization
Earning a third-party certification can be helpful, too. Options include:

대학에서 살펴봐야 할 사항

Students should seek colleges offering majors in data science, computer science, artificial intelligence, or machine learning
Look for programs that have internships or other opportunities where you can gain practical experience, especially related to AI and ML
Consider applying to a dual BS/MS program to save time on completing your master’s
Decide if you want to do online or hybrid courses
Always compare the costs of tuition and other fees. Review your options for scholarships and financial aid
See if the program has any partnerships with companies that hire grads!
Take note of graduation and job placement statistics for alumni

고등학교와 대학교에서 할 일

High school students should take courses in math (including differential calculus), English, communications, and information technology (especially AI and ML, if possible)
High school students who don’t have access to AI/ML courses can study on their own to start building a foundation. Consider joining or forming a computer club!
Knowledge of Python and SQL will come in handy later, and these can also be learned through self-study
Apply for a bachelor’s program in computer or data science or a related field, with a focus on machine learning. Consider applying to a dual BS/MS program to save time on completing your master’s

A master’s degree may not be necessary for every position but it can boost your credentials and may enable you to apply for better-paying starting positions

Look for part-time jobs where you can rack up relevant work experience. You’ll need years of experience to be considered for a managerial position (including experience supervising others and leading teams)
Apply for relevant internships, through your school or on your own
Read magazines and website articles related to machine learning. Consider doing ad hoc courses via Coursera or other sites for more structured learning
Request an informational interview with a working Machine Learning Manager

일반적인 로드맵

첫 직장을 구하는 방법

Check out job portals like Indeed.com, LinkedIn, Glassdoor, Monster, CareerBuilder, SimplyHired, or ZipRecruiter
Don’t expect to start at a managerial level! Unless you already have a few years of relevant work experience, you’ll need to apply to entry-level positions first
Consider relocating close to a tech hub city like Austin, Dallas, Raleigh, San Jose, or Charlotte
Stay in touch with classmates and use your network to get job tips. Most jobs are still found through personal connections
Ask your instructors, former supervisors, and/or coworkers if they’re willing to serve as personal references. Don’t give out their personal contact information without prior permission
Check out some Machine Learning-related resume examples and sample interview questions, including basics like “What Are the Different Types of Machine Learning?” or more advanced topics such as “How Will You Know Which Machine Learning Algorithm to Choose for Your Classification Problem?”
Practice doing mock interviews with your school’s career center (if they have one)
Dress appropriately for interviews and show your enthusiasm for and knowledge of the AI/ML field

사다리를 오르는 방법

It takes years of education and work experience to work your way up to becoming a Machine Learning Manager. Once you’re there, you’re already pretty high up, but there are still opportunities for advancement and salary increases
Higher-level job titles include Senior Machine Learning Manager and Director of Machine Learning or Head of Machine Learning
Managers may also seek out cross-functional leadership or industry specialization roles. Some opt to switch to pure research and development positions
Let your supervisor know you’re interested in career progression and ask for their advice
Most ML Managers have a graduate degree, but for those who don’t, earning a master’s will be a great way to boost credentials and qualifications
Add value to the organization by incorporating ML wherever it can be of use. Communicate with leadership and stakeholders to ensure ML objectives and benefits are understood
Lead teams effectively and ensure projects are kept on schedule and on-budget
Keep track of AI and ML trends and challenges. Stay up-to-date on the newest software
For those working at smaller organizations, you may have to apply to work for a larger or different type of organization to make a bigger paycheck or reach higher career goals

For example, managers who work for governmental agencies may earn a more lucrative salary at a private tech company

Completing advanced third-party certifications can be helpful, too. Options include:

Of course, ML Managers with a strong business background may thrive as entrepreneurs who launch their own AI or ML-related businesses instead of working for someone else!

Consider Stanford professor Andrew Ng, a prominent ML entrepreneur and co-founder of Coursera and Google Brain, who has a net worth of ~$122 million!

권장 도구/리소스

웹 사이트

도서

Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, by Aurélien Géron
Machine Learning For Dummies, by John Paul Mueller
The Hundred-Page Machine Learning Book, by Andriy Burkov

플랜 B

Machine Learning is a fascinating field but it takes years of education and work experience to qualify for a manager position. There are numerous related career options to consider, some of which may require less time to qualify for. By the same token, a few of these roles may serve as a stepping stone to becoming an ML Manager later!

AI Prompt Engineer
빅 데이터 엔지니어
Business Intelligence Developer
Computer Programmer
Computer Systems Analyst
Database Architect
데이터 과학자
정보 보안 분석가
Mathematician
Machine Learning Engineer
로봇 공학 엔지니어
Software Architect
Web Developer

뉴스피드

편집자 Gladeo

주요 채용 정보

온라인 강좌 및 도구

연봉 기대치

$142K

$172K

$210K

New workers start around $142K. Median pay is $172K per year. Highly experienced workers can earn around $210K.

출처: 캘리포니아 주, 고용 개발부

연봉 기대치

$131K

$169K

$214K

New workers start around $131K. Median pay is $169K per year. Highly experienced workers can earn around $214K.

출처: 캘리포니아 주, 고용 개발부

머신 러닝 관리자

스포트라이트

뉴스피드

연봉 기대치

연봉 기대치

주요 채용 정보

온라인 강좌 및 도구

연봉 기대치

연봉 기대치

유사 경력