Artificial Intelligence Roles Explained: The Key Jobs and Functions in AI

Introduction to AI Roles

The adoption of artificial intelligence (AI) across industries is accelerating, creating high demand for professionals with the right skills to take on essential AI roles. From researchers advancing the state-of-the-art in machine learning, to engineers building and deploying AI systems, a variety of positions are emerging with unique responsibilities.

Key AI roles focus on conducting research to expand capabilities, developing applications leveraging AI, managing projects and products, analyzing data to train algorithms, and more. Core skills like programming proficiency, statistical reasoning, and creativity are crucial for most roles.

As companies race to integrate AI and data-driven decision making into business operations, they require talented individuals who can translate AI innovations into tangible business value. The talent shortage has driven up salaries and incentivized education programs aimed at developing new AI talent pipelines.

AI Researchers

AI researchers spearhead scientific studies and experiments to create new machine learning models and advance the current limitations of artificial intelligence. Their focus areas span neural networks, natural language processing, computer vision, robotics, and more.

Typical responsibilities include:

Conducting studies and literature reviews to identify research gaps and opportunities
Designing and running experiments using statistical methods and big data pipelines in Python and R
Developing novel machine learning algorithms, convolutional neural networks, recurrent neural networks, and other AI techniques
Publishing findings in peer-reviewed academic journals and presenting at conferences
Leading groups of researchers and collaborating across disciplines

Most positions require a PhD in computer science, mathematics, statistics, or a related technical field. AI research roles are concentrated in technology companies like Google Brain, Facebook AI Research, DeepMind, and OpenAI as well as university labs.

AI Engineers

AI engineers apply research breakthroughs to build real-world AI products and services for business and consumer use. They integrate artificial intelligence models like neural networks into applications, websites, devices, and other software.

Common responsibilities include:

Developing AI applications like chatbots, recommendation engines, image recognition, using languages like Python, Java, and C++
Selecting appropriate data models and machine learning algorithms like random forests, SVMs, neural networks
Building data pipelines to train, evaluate, and optimize AI models
Deploying AI systems in client/server environments or embedded systems
Collaborating with data scientists, product managers, and other roles

AI engineering requires a strong computer science or software engineering background, along with coding skills. Many companies hiring AI engineers also list mathematics, statistics, and machine learning competency as preferred qualifications.

AI Project Managers

AI project managers oversee the development of artificial intelligence capabilities and systems across the project lifecycle. They coordinate cross-functional teams of data scientists, engineers, researchers, and other staff to achieve project goals.

Typical responsibilities involve:

Developing project plans, budgets, timelines, and deliverables
Determining project resources, equipment, software, and data needs
Leading design reviews, sprint planning, and status updates
Monitoring progress, resolving issues, and mitigating project risks
Managing customer expectations and requirements

AI project managers require a blend of technical competence and soft skills in communication, organization, and leadership. PMP certification and previous experience managing software/IT projects is valued.

AI Product Managers

AI product managers bridge the gap between engineering and business teams to drive the strategy and roadmap for AI products. They gather market insights and user feedback to inform development priorities and convey business needs to technical staff.

Common responsibilities include:

Defining the vision and development roadmap for AI products
Conducting user research, market analysis, and competitive benchmarking
Translating business requirements into user stories and product specifications
Coordinating go-to-market planning with sales and marketing
Tracking key performance metrics and optimizing based on data

AI product managers should possess business acumen, user empathy, and enough technical literacy to interface with engineers. Previous experience in product management or related business roles is key.

AI Data Scientists

AI data scientists enable the development of machine learning models by preparing, cleaning, labeling, and analyzing datasets. They identify patterns and insights that inform the selection, training, evaluation, and interpretation of AI algorithms.

Responsibilities may involve:

Developing data collection strategies and building data pipelines
Processing raw data and transforming it into usable formats
Performing exploratory analysis to surface patterns, outliers, and relationships
Training and selecting machine learning models like random forests, SVM, neural networks, K-Means clustering with the optimal data
Communicating data limitations, gaps, quality issues, and results

The role requires skills in statistics, programming (Python, R), and tools like SQL, Spark, Hadoop, and cloud data platforms. Proven ability in statistical modeling and machine learning algorithms is mandatory.

Essential AI Role Skills

While specific skills vary across artificial intelligence roles, some key technical and soft skills are universally valued. Here are the top capabilities that AI professionals need to thrive:

Technical Skills

Programming: Fluency in Python, R, Java, C++
Libraries: NumPy, Pandas, TensorFlow, PyTorch, Keras, OpenCV, Scikit-Learn
Tools: Jupyter, Colab, Git, Docker, Kubernetes, Spark, Hadoop
Cloud platforms: AWS, GCP, Azure, Watson, Dialogflow, BigQuery
Data engineering: SQL, NoSQL, data pipelines, streaming

Math and Statistics

Linear algebra, multivariate calculus, discrete math
Statistical modeling, simulation, analysis, and interpretation
Optimization, probability, Bayesian statistics
Quantitative reasoning and computational thinking
Bias-variance tradeoff, overfitting, and other ML theory

Communication

Explaining complex topics clearly and simply to technical and non-technical audiences
Collaborating effectively across distributed teams and roles
Writing clean, organized technical reports and presentations
Active listening and requirements gathering skills
Fluency in English reading and writing

Creativity

Applying design thinking principles to solve problems
Thinking outside the box and developing innovative solutions
Dealing effectively with ambiguous, undefined situations
Making connections across disciplines to generate unique ideas
Identifying opportunities and pushing boundaries beyond incremental progress

Domain Expertise

Understanding industry verticals like finance, healthcare, manufacturing, agriculture
Navigating industry regulations, data standards, and ethics
Identifying where AI can provide the most business value
Competitive analysis and market positioning
Translating real-world needs into technical solutions

AI Role Demand and Growth

Surging adoption of AI across virtually every industry is creating tremendous demand. New emerging roles, significant salary growth, and education initiatives are all factors influencing the AI talent landscape.

Growing Demand

81% of enterprises are implementing AI solutions by 2022 per IDC
AI market estimated to reach $190 billion globally by 2025 according to MarketsandMarkets
AI job postings increased by over 70% from 2015 to 2020 per LinkedIn
Shortage of qualified candidates persists, especially for engineering roles
Investments focused on integrating AI for business value

Emerging Roles

Several newer AI roles have emerged including:

AI trainers to teach AI systems using reinforcement learning
AI auditors to test systems for fairness and mitigate bias risks
AI ethicists to develop responsible and ethical AI policies
Conversational designers for natural language chatbots
Computer vision engineers focused on perception AI

Higher Salaries

Average machine learning engineer salaries increased 344% from 2015 to 2019 per Hired.com
Median base salary for AI specialists over $350,000 according to IEEE
Big technology firms competing aggressively for talent with compensation
Increased remote work expands access to global talent markets
Equity, signing bonuses, and performance incentives common

Education Initiatives

Top education programs are emerging globally including AI degree programs at MIT, Stanford, and Carnegie Mellon, coding bootcamps like Springboard and Metis, massive open online courses (MOOCs) on Coursera and Udacity, and workshops like Fast.ai and AI Hackathons.

Geographic Trends

Silicon Valley still the epicenter of AI talent, followed by New York and London
Second tier hubs gaining more jobs supporting remote AI work
Toronto, Montreal, Vancouver emerging AI hubs in Canada
UK and Israel have strong university programs feeding talent
Asia-Pacific experiencing rapid growth in AI investments

AI Role Impact on Businesses

The influx of artificial intelligence talent fills critical needs for businesses embracing AI. Key benefits include:

Improved Efficiency

Automating repetitive, routine tasks to reduce labor costs
Providing 24/7 availability of virtual agents for customer service
Accelerating document processing and data entry workflows
Enabling staff to focus on higher-value work

Data-Driven Decisions

Generating predictive analytics to anticipate future outcomes
Building prescriptive models that recommend optimal actions
Applying sentiment analysis to understand customer opinions
Creating forecasting algorithms to improve planning
Optimizing marketing spend allocation through attribution modeling

Personalization

Delivering customized content recommendations to each user
Tailoring shopping experiences based on buyer persona
Crafting targeted promotions and special offers
Personalizing search results based on context
Configuring products/services to match user preferences

New Products and Revenue

Developing innovative computer vision and NLP applications
Using AI to accelerate development cycles
Monetizing data through new analytics services and platforms
Creating viral social media products enhanced by AI
Enabling futuristic offerings like self-driving vehicles

Process Optimization

Improving supply chain efficiency through AI planning
Enabling predictive maintenance to reduce equipment failures
Automating QA testing to boost software quality
Detecting fraud earlier to mitigate financial risks
Optimizing manufacturing throughput and yield via AI

Conclusion and Summary

There is a wide range of critical yet distinct artificial intelligence roles needed to unlock the full potential of AI for businesses. While technical skills are mandatory, soft skills and business acumen determine which AI professionals will thrive in these domains.

The talent shortage has created abundant opportunities within AI research, engineering, project/product management, data science, and more. As organizations accelerate AI adoption, they require skilled talent that can translate innovations into measurable business value. AI will undoubtedly transform industries, but it is the people behind the AI that will determine the nature and impact of that transformation.

Have a great idea for an AI product or service? The AI Marketplace Builder from Marketsy.ai makes launching your own AI-powered business faster and easier than ever before.