AI and Machine Learning for Finance — Career Outlook and Learning resources

Machine learning and AI applications enable banking institutions and financial services companies to offer a far more streamlined process with reduced risks and personalized customer experience.

AI and Machine Learning for Finance — Career Outlook and Learning resources
Image by Jamie Street

Financial Institutions and FinTech startups are banking heavily on Machine Learning and AI, creating more and more tech-savvy jobs to speed up their growth. Therefore, the demand for high-quality financial engineering, financial analysis, algorithmic trading, risk management, and forecasting has significantly increased over the last few years.

The obvious benefits of using Machine learning for finance are difficult to overestimate. The most important use of the Machine Learning algorithms is dealing with a myriad of tasks to detect work patterns and correlations among vast amounts of information, events, operations, and sequences.

Machine learning provides high-level accuracy in drawing insights and making predictions. That's part of why Machine Learning and AI is strongly used in automation, customer support optimization, credit risk management, portfolio management, stock market forecasting, security issues, etc.

It is indicative that machine learning is an indispensable tool for the Finance industry. This post briefly focuses on ML/AI Career Outlook in Finance technology. Next, this article outlines the best learning paths to attain the Machine Learning knowledge and high-end deep learning skills required for tackling real-world challenges in banking.

Let's take a quick glance at the prospects that the applications of AI and ML opens in the finance industry.

note: we use simple language for novices

Machine Learning for Finance - Career Outlook

Machine Learning and AI are critical for financial institutions to stay competitive and thrive. It is reported that financial companies, including banks, employ around 60% of all professionals skilled in Machine Learning and AI systems.

Machine Learning for Finance
Image by Juniperresearch

With the radical growth of data science, machine learning, and cloud computing, the employment trend in the finance industry is proliferating. Financial service companies are constantly enlarging their use of machine learning to optimize customer experience and back-office operations.

Currently, there are thousands of unique job level postings in the Finance Industry to help companies offer faster support, customer retention, smooth processes, security, etc.  

Machine learning in the finance industry is truly revolutionary, making impossible things possible. That's why there has also been a steady growth in Machine Learning careers that combines data science, applied research, and feature engineering.

Here are some of the top job titles that help companies provide safer, more innovative, and more efficient services.

  • Machine Learning Engineer
  • Machine Learning Scientist
  • Financial Data Scientists
  • Data intelligence analyst
  • Financial engineer
  • Quantitative analyst
  • Quantitative researcher
  • Investment analyst

According to Glassdoor, the average salary of a machine learning engineer in the US is $​​1,31,000. FAANG companies pay significantly higher in the range of $170,000 to $200,000.

We know the options out there; prerequisites and the skills needed for a machine learning career in the finance industry.

Career Skills You Need to Learn

There are various kinds of Machine Learning jobs needing specific skills and experience. Machine Learning jobs in Finance seek applicants with a solid background in mathematics, experience in machine learning, deep learning, neural networks, and statistical programming skills in Java, Python, R and Scala. It also helps tremendously to have some data engineering skills.

Finance companies expect Machine learning Engineers/ scientists to have extensive knowledge and experience in computer vision, probabilistic graphical models, reinforcement learning, and natural language processing. Knowledge of benchmarking, parallel computing, and distributed computing are a plus.

We have written this review-driven guide to provide the career outlook with the best programs to learn Machine learning for finance from notable machine learning educators.

Recommended Prerequisite → Mathematics for Machine Learning
Also, refer to the resource section at the tail end of this piece, where we usually add adjunctive learning resources.

Now, without further ado, let’s get started.

Top 5 Best Machine Learning for Finance Courses

ML Engineers working in the finance industry are specialists. It’s clear that Machine Learning engineers are in high demand, the road however, to becoming a ML specialist isn’t quite as straightforward. It involves techniques that can be exceedingly challenging to understand without an effective teacher.

There are a plethora of ways to receive a Machine Learning education, but how do you know which one is right for you?

Most Machine Learning courses include hands-on activities and projects that simulate real day-to-day activities on the job. This is because many courses aim to have learners ready for an entry-level job in just a few months, and some even help you land that job.

While some educators are known for their long and theoretical approach to study, these learning tracks are focused on hands-on training with the tools, problems, and inner workings of a typical MLOps role in finance.

This list will help you figure out which Machine Learning for Finance course best meets your needs.

Artificial Intelligence for Trading

Udacity provides one of the best AI Quantitative Trading Nano-degree program in partnership with the WorldQuant that offers a job assistance. Over the course of six months, learners explore an expert-curated curriculum that emphasizes hands-on deep learning projects and career-related coursework to master AI algorithms for trading in Python.

Through on-demand videos, in-depth articles, and technical guidance from industry experts, students build skills in quantitative analysis, including data processing, trading signal generation, and portfolio management.

Career Outcome 👍🏾

This Nanodegree program will equip you with the most coveted data and AI skills to pursue a career in AI quant trading. You'll gain a strong technical foundation in quantitative analysis, programming, statistical modeling, and master AI applications in quantitative finance.

It is aimed at professionals in adjacent fields like data science, deep learning, computer vision, reinforcement learning, and more. It is popular amongst machine learning professional who want to upskill as well as data scientists who want to bootstrap their quantitative skill set.

This program will help you build an impressive portfolio of real-world projects and prepare you for a rewarding career at hedge funds, investment banks, and FinTech startups.

This program also provides access to leading experts in the field for personalized project and career support.
Artifical Intellifence for Trading
Image by Udacity
Educational Objectives

The Nanodegree program comprises learning content and curriculum to support eight (8) projects.

  • Basics of Quantitative Analysis and Quantitative Trading.
  • Advanced Quantitative Trading
  • Factor Investing and Alpha Research
  • Sentiment Analysis with Natural Language Processing
  • Advanced Natural Language Processing with Deep Learning
  • Combining Multiple Signals
  • Simulating Trades with Historical Data
Is it right for you?

This program is suitable for learners with intermediate-level Python programming experience and familiarity with statistics, linear algebra and calculus.

Machine Learning with Python

Developed by DataCamp, this course is intended for practitioners who want to learn how to build models and predict stock data values using linear models, decision trees, random forests, and neural networks.

The course approaches Machine Learning and AI from a business perspective and equips machine learning practitioners with the scientific knowledge of how to make predictions using machine learning techniques to make stock trading strategies profitable.

Career Outcome 👍🏾

By the end of the course, you’ll have had hands-on experience in using machine learning algorithms and tree-based machine learning models to predict future values of a stock's price. You will also gain skills to learn when and how to use forest-based machine learning methods for regression and feature selection.

You will learn the key concepts of Time series data and understand how to use linear models, decision trees, random forests, and neural networks to predict the future price of stocks.

Machine Learning for Finance
Image Source: DataCamp
Educational Objectives

Unlike some of the other courses that teach students how to do the job of an ML Engineer, this course helps learners understand how to prepare features for linear models, xgboost models, and neural network models.

Is it right for you?

This ML course is suitable for Python programmers with a functional knowledge of supervised learning with scikit-learn. It is good for individuals with a limited background in reinforcement learning.

Fundamentals of Machine Learning in Finance

This NYU program offers an accessible introduction to fundamentals of machine learning in finance, Supervised and Unsupervised Learning, and Reinforcement Learning. You will also learn to use Python packages to implement ML algorithms in Finance.

Career Outcome

This course equips learners with skills in supervised, unsupervised, and reinforcement learning, with a project applying unsupervised learning to implement a simple portfolio trading strategy.

You will gain skills to solve practical Machine Learning problems and understand the fundamental concepts using open-source ML algorithms and tools that fit with financial data.

Machine Learning in Finance
Image by Coursera
Educational Objectives

The course is offered as a MOOC, which means you can learn independently from pre-recorded lectures and readings.

  • Basics of Supervised Learning in Finance
  • Core Concepts of Unsupervised Learning
  • PCA & Dimensionality Reduction
  • Data visualization & Clustering
  • Using sophisticated ML algorithms
  • Sequence Modeling
  • Reinforcement Learning
Is it right for you?

This intermedial-level course is good for Python programmers with a functional knowledge of numpy, pandas, and jupyternote book, linear algebra, statistics and calculus.

Machine Learning and Reinforcement Learning in Finance Specialization

NYU’s Specialization courses in Machine Learning and Reinforcement Learning is for individuals looking to learn the applications of Machine Learning and Deep Learning in finance, including techniques to solve practical problems in finance.

This learning program is broad and gives students a taste of the various elements to use machine learning techniques, such as regression and classification. It goes over problem identification with deep learning techniques and architectures and their applications in finance.

By the end of the course, you’ll become the ML advocate for your business and craft the right applications and models.

Career Outcome 👍🏾

The program gears to machine learning professionals, analysts and data scientists to build a deeper understanding of supervised learning (regression and classification) and unsupervised learning, and the useful applications of both.

You will learn how to build machine learning models and gain problem-solving skills to construct a problem-solving map with deep learning and neural networks for financial engineering.

Reinforcement Learning in Finance
Image by Coursera
Educational Objectives

This program will help you obtain a deep understanding of the various machine learning and deep learning approaches in financial technology.

  • Classical Machine Learning models
  • Supervised Machine Learning
  • Unsupervised Learning, Dimensionality Reduction
  • Neural Networks and Deep Learning
  • Convolutional Neural Networks
  • Advanced Recurrent Architectures
  • Neural Language Processing (NLP)
  • Reinforcement Learning in Finance
  • Applications of Reinforcement Learning (P-2-P Lending and Cryptocurrency)
Is it right for you?

This program is intended for learners who have knowledge of basic probability, statistical techniques, linear regression, calculus; linear algebra. You'll need a basic familiarity with machine learning to understand how Machine Learning and Reinforcement Learning can improve practice in financial engineering.

Machine Learning for Trading Specialization

Google Cloud and the New York Institute of Finance provides one of the longest-running Machine Learning Specialization in Trading for finance and machine learning professionals to upgrade their skills for quantitative trading strategies.

This specialization provides is a great approach to learn by doing, with multiple labs and projects designed to mimic real-life professional scenarios to structure and apply techniques used in reinforcement learning (RL) strategies for trading.

The courses are designed for independent individuals who enjoy learning through the material at their own pace. By the end, you'll have gained advanced technical skills to create quantitative and algorithmic trading strategies.

Career Outcome

The specialization does not include a job guarantee or advanced technical guidance but aims to equip learners with job ready ML and Cloud Computing skills.

You will learn to create quantitative strategies and algorithmic trading techniques using Python. These skills are indispensable in finance technology and you can show your skills to create and enhance quantitative trading strategies with machine learning and deep learning in your ML/ Data Science Portfolio for job opportunities.

Machine Learning for Trading
Image Source: Coursera
Educational Objectives 👍🏾

This course is suitable for understanding the most important concepts of trading and Cloud Machine Learning with the GCP.

  • Supervised/ Unsupervised and Regression/ Classification
  • Concepts of Quantitative Trading
  • Neural Networks and Deep Learning
  • Using Google Cloud Platform to build machine learning models
  • Keras and TensorFlow for building model
  • Reinforcement learning
  • Design quantitative trading strategies.
Is it right for you?

This specialization program is suitable for those who understand the foundations of Machine Learning at an intermediate level and possess the knowledge of financial markets. You should have the functional knowledge of Python programming and a solid background in mathematics and statistics to work ML libraries such as scikit-learn, StatsModels, and Pandas. Experience with SQL will be helpful to get ahead faster.


It’s actually no surprise that we can use convolutional neural networks for time series analysis. It might seem strange because they are used for image-related tasks, but researchers are using convolutional networks for sequence classification.

Stock prices are a sequence and we can use them to make predictions. That's part of why Tensorflow.js library is commonly used to test out a prediction model for the stock prices.

We hope your journeys will go as you hope, and that the adjunctive resources listed in this article will equip you for a rewarding career in finance technology.

Your Guide to Machine Learning Career

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