I’m sure you know this, but you’re pursuing a career in a rapidly growing field with a ton of demand for talented, qualified people — and it’ll only speed up in growth over the next 5-10 years.
There are two basic approaches you can take here, depending on whether you want to pursue a career as a data science practitioner, or if you’d prefer a more general finance / business specialization (in which case you’d be positioning yourself to be more of an interface between the business and the data science practitioners).
Both paths are good; if you find that you really enjoy computer science, statistics, and logical problem solving I’d head more toward practitioner, if working with people and process (and managing a team) are more appealing, I’d head toward the latter.
Practitioner approach: start pursuing data science and analytical projects on your own, in addition to school; look for an internship with a Business Intelligence, Data Science, or Analytics team in a decent-sized company. For your first job, look for jobs in those functions (they might be in finance, or IT, or marketing or elsewhere; you’ll be best positioned for finance roles, but data science skills are very transferrable).
Interface / translator approach: look for roles in finance and accounting and begin to integrate these skills into your work, as Ashleigh was describing.
Here are some exciting career opportunities that you can pursue with your background in accounting and computer science. You might also consider roles like a financial data analyst, financial statistician, or a financial software developer.
Consider becoming a Financial Analyst. This role allows you to harness data analysis and modeling techniques to provide valuable insights and recommendations for financial decisions. As a financial analyst, you could find yourself working in diverse fields like investment banking, corporate finance, portfolio management, or risk management. Earning a certification like the Chartered Financial Analyst (CFA) could significantly boost your credibility and job prospects in the finance industry.
How about a career as a Data Scientist in the finance sector? This role involves using advanced data science methods like machine learning and artificial intelligence to tackle intricate and unique financial problems. As a data scientist in finance, you could work on fascinating projects like fraud detection, credit scoring, market prediction, or customer segmentation. To excel in this field, you might need to earn a master’s degree or a PhD in data science or a related field like computer science, math, or statistics. Additionally, honing technical skills like Python, R, SQL, and data visualization could be beneficial.
Another rewarding path is becoming a Financial Data Engineer. This role involves creating and managing data pipelines and infrastructure for financial data analysis and processing. As a financial data engineer, you could be responsible for tasks like data collection, integration, storage, quality assurance, and security. To thrive in this role, you would need strong programming and database skills, along with a good understanding of cloud computing and big data technologies.