Top 8 Data Science Skills Required in the Fintech Industry by 2024

By Aamna Babar | 12th April 2023

With cutting-edge goods and services including mobile banking, internet lending, and robo-investing, the growing FinTech industry hopes to upend conventional finance. Fintech, a portmanteau of “financial technology”, refers to firms using new technology to compete with traditional financial methods in the delivery of financial services. Artificial intelligence, blockchain, cloud computing, and big data are regarded as the “ABCD” of fintech.

The fintech industry has acknowledged the potential of data scientists and other data professionals to advance global financial transactions and payment systems.Working in data science can provide you with the chance to use data-centric methods to change the financial sector. In this article, we have enlisted the top data scientist skills required to land a job in the FinTech industry.

1.  Financial Modeling

Financial modeling is a representation in numbers of a company’s operations in the past, present, and the forecasted future. Such models are intended to be used as decision-making tools. Company executives might use them to estimate the costs and project the profits of a proposed new project.

2.  Machine Learning

To uncover patterns in data sets and make insightful predictions, data scientists need a solid grasp of machine learning techniques. Unquestionably, machine learning in FinTech reduces the risk factors for engagement rings and facilitates loan approval. It lowers the risk factor by employing the widely used credit scoring methodology.

3.  Programming Languages: Python and R

Statistics is the heart of data science programming and thus it is a must for a professional to develop expertise in Python and R language. It is necessary to learn these programming languages on a big data system landscape like Hadoop, Oracle, or SAP HANA so that professionals can build industry use-cases, related to workforce, customer, and marketing analytics.

4.  Time Series Analysis

Fintech companies deal with time-series data such as stock prices, interest rates, and transaction volumes. Therefore, a data scientist in fintech should have experience with time-series analysis techniques such as ARIMA, VAR, and GARCH.

5.  Natural Language Processing (NLP)

Fintech companies use NLP techniques to analyze customer feedback, news articles, and social media data to understand market sentiment, customer behavior, and trends. Therefore, a data scientist in fintech should have experience with NLP techniques such as sentiment analysis, text classification, and entity recognition.

6.  Deep Learning

Fintech companies use deep learning techniques to build predictive models for credit risk, fraud detection, and investment recommendations. Therefore, a data scientist in fintech should have experience with deep learning algorithms such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Generative Adversarial Networks (GANs).

7.  Cloud Computing

Fintech companies deal with large and complex datasets, which require scalable computing infrastructure. Therefore, a data scientist in fintech should have experience with cloud computing platforms such as Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP).

8.  Data Visualisation

It is necessary for a data scientist to master the skills of communicating data-driven insights in a visually effective manner. Data scientists should be capable of describing the findings in a manner that can be interpreted by both technical and non-technical audiences. Thus, in-depth knowledge of various data visualization tools like Tableau, D3.js, and ggplot2 helps data scientists provide clear insight into their data-driven insights.


In conclusion, the fintech industry requires data scientists to possess a diverse set of technical  skills to leverage the vast amounts of financial data for better decision-making. If you are a young professional or a data scientist looking to gain these skills, you can refer to a number of resources online. For starters, atomcamp’s Data Science Bootcamp provides an end to end accelerated learning program for those looking to enter data science in any domain. Other than this, there are a number of free courses offered at the Harvard University or youtube channels that stream technical courses, for example Through these resources, you can upskill your skillset to be ready for your future career in fintech.