Exploring data science and machine learning career paths By: Kiana Seitz July 23, 2024 Estimated reading time: 11 minutes. How many times have you gone down the YouTube rabbit hole as the platform continues to recommend more and more videos you’re interested in? How do streaming services like Netflix and Disney Plus know exactly what shows you’d like? In both cases, platforms tailor “recommended for you” suggestions in your feed, usually nailing your preferences right on the head to keep you watching for longer. These examples illustrate how data plays an impactful role in our society. As our computing knowledge and technology grow, we increase our capability to collect, store, and analyze data. This means our world needs people who excel in numeracy and analysis and those with machine learning skills to help us take data and turn it into valuable insights for our businesses and organizations. The need for data scientists and engineers across all industries will grow as we amass more data and seek new ways to use it to affect change. Let’s explore data science and machine learning career paths, highlighting key industry trends and the differences between entry, intermediate, and senior roles. We’ll also explain what to expect in these paths and how you can get the skills you need for this growing, in-demand career trajectory. Introduction to data science and machine learning careers Data science and machine learning, while sometimes used interchangeably, are actually two separate concepts: Data science Machine learning Data science encompasses machine learning and other processes and tools that help bring order and understanding to large amounts of data. Machine learning is a branch of data science and artificial intelligence that uses algorithms to look for patterns in data and make decisions or predictions. Data science is essential for businesses because it supports: Product development: Data analysts gather and analyze data (like user behaviour, customer profiles and demographics, and browsing history) that can teach you more about a target customer group and make predictions of their needs, your industry, and your business. Business process optimization: With detailed data analytics about customer behaviour or business processes (like time to resolution for customer support calls, or customer satisfaction), you can uncover ways to increase efficiency in business operations, such as increasing the time to resolution for support calls or streamlining new client onboarding to increase their satisfaction. Customer-centric decision-making: Using data from user activity (like website clicks, buying behaviour, and comments on social media) and behaviour and predictive analysis, you can make business decisions that positively impact target customers. Read more about the opportunities and challenges of using data science and AI The evolution of the data science job market The term “Data Science” was first coined in the 1950s to describe the concept of understanding and interpreting large amounts of data. At that time, computing was gaining significant momentum and large quantities of digital data (“Big Data”) were being created and stored. Over time, data science has evolved from an interpretation engine to include computer science, statistical methodology, artificial intelligence, and machine learning. With the rise in popularity of computers and the internet, so did the data that could be stored in these spaces, and the need for data scientists to work with this data increased. Key dates in the history of data science 1950s - “Data Science” coined by John Tukey 1960s - Big Data grew as computers were able to store and process larger amounts of data 1970s - Data mining emerged to create a process to discover patterns in large datasets 1990s - The internet boom and a sharp increase in online data available 2000s - Big Data supported new technologies for data processing and analysis 2010s - Machine learning is developed to use algorithms to analyze data and make predictions 2020s - Artificial intelligence and blockchain are emerging as new tools for the future of data science Emerging trends and future industry outlook Data science has already evolved so much in the past 70 years, and the industry's continual growth will necessitate more highly skilled data scientists and machine learning engineers to support this growth. Some emerging trends we’re watching in this industry include: Small data Data science and machine learning were designed to process big data, but their use cases for small data will be just as crucial in the coming years. Small data allows data scientists to process smaller amounts of data quickly in bandwidth-constrained environments. We’re already seeing this in self-driving cars, which need to process environmental information locally. In an emergency, they can’t rely on a connection to the cloud or a centralized server to retrieve and process data to make driving decisions. Data-driven customer experiences Personalization is key to driving sales. Data scientists use data and machine learning to understand their customers better and make assumptions and predictions. These insights can help businesses predict when a specific customer is likely to make their next purchase, what they’ll buy, and how much they're likely to spend, among other valuable marketing and sales insights. It can also display customized content based on their history (like showing targeted ads to those who have visited their website or made a specific purchase). Data in the cloud Cloud-based computing has made quite a popular entrance into the data science field, creating centralized data repositories that can be easily vetted, categorized, organized, and analyzed. We expect it to continue evolving and create more opportunities to protect data and perform tasks more efficiently. Artificial intelligence AI will continue to play an increasingly important role in data collection, analysis, interpretation, and prediction. With the support of machine learning and advancing IT infrastructure, AI will develop into a more powerful tool, helping businesses gain deeper insights into the state of their customers, businesses, and the world around them. You’ll learn about Large Language Models and other trends in the AI module of our Data Science Bootcamp. Learn more here. Entry-level opportunities in data science There are an estimated 67,600 data scientist jobs throughout Canada today. The job market is expected to grow by 29,000 new jobs between 2022 and 2031, with 32,700 new job seekers ready to fill these roles. This career path typically requires: A university degree or bootcamp diploma Experience in programming Understanding and experience in statistical modelling and machine learning Excellent skills in digital literacy, numeracy, and systems analysis Success in the data science career path is often partially due to the individual's unique personal qualities and technical experience, including innovative and analytical thinking, coupled with a high degree of creativity, independence, and adaptability. A strong interest in investigation, adherence to procedures and routines, and a proactive approach to goal achievement are also characteristics of those who thrive in this field. In Canada, the data science career path and industry are not regulated and don’t have any mandatory professional certification requirements. Many data science roles are full-time (35-40 hours per week), and they earn an annual wage of $77-116K. Data analyst (as an entry-level role) Data analyst roles range from entry-level to senior. While they all perform the same basic tasks, entry-level roles may include additional mentoring or support from senior co-workers. As a data analyst, you may be asked to support with the following tasks: Collection - Collecting data through cloud sources, surveys, or purchasing data from third parties. Cleaning - Manually checking data to ensure it’s error-free, duplicates are removed, and data is interpretable. Modelling - Structuring datasets into categories and tags (and other relevant structures as needed). Analysis - Interpreting data to look for trends and patterns. Insights - Using analysis results to derive actionable insights. Visualization - Turning data into visual graphics and representations to share with stakeholders. A junior data scientist will require formal computer science or data science education from an industry leader or educational institution. A recognized bootcamp-style certification in data analytics or science can also help you upskill if you currently have other IT education and experience. Read more about how to get your first data science job. Pathways from data science to machine learning Getting an entry-level machine learning-focused job can be challenging, but if you are passionate about the field, starting in an entry-level or junior data scientist role can help you get there. It is possible to obtain an entry-level job in machine learning like Lighthouse Labs grad Dhruvin. Look for an entry-level data scientist job with a good mentorship program or opportunities to upskill with the support of more senior data scientists on your team. You can also enroll in flexible bootcamp-style programs to get the up-to-date skills you need for machine learning. Lighthouse Labs’ 12- and 30-week Data Science Bootcamps have an entire module dedicated to machine learning. You’ll explore ML through scikit, Tensorflow, and Keras to learn about supervised learning, unsupervised learning, deep learning, NBP, time series, and recommender systems. Download your copy of the full Data Science Bootcamp Curriculum to learn more about how Lighthouse Labs can support your educational journey. DOWNLOAD CURRICULUM Intermediate career paths Once you have several years of experience in data science under your belt, you can make the jump to intermediate-level positions. Two common mid-level careers in data science are data scientist and machine learning specialist, each earning around $74-100K or more per year. Data scientist A data scientist role is often a direct career progression for those who have proven their skills in entry-level analyst roles. This usually requires at least two or three years of hands-on experience before you qualify for an intermediate-level data scientist role. As a more senior-level staffer, you’ll work with less oversight. As a data scientist, you’ll leverage your formal education and hands-on skills in computer science, math, statistics, and modelling to drive and uncover new strategies. You will confidently share your findings with companies, empowering them to enhance their practices and systems. Helpful skills to master to achieve an intermediate-level data scientist role may include: Better understanding of how data analytics affects or influences business practices. Ability to work independently with fewer check-ins with a supervisor. Advanced, independent problem-solving skills.** Ability to prioritize your tasks effectively without needing micromanaging. Learn more about what it takes to become a data scientist Machine learning specialist or engineer An intermediate-level machine learning specialist often includes many of the same responsibilities as a data scientist, with additional machine learning practices and analysis. You’ll also need anywhere from 2 to 5 years of experience in the industry to meet the demands of a machine learning career path. You must have proven machine learning skills gained through mentorship in previous roles or by taking an intensive machine learning bootcamp or program. The machine learning part of your job will entail creating, testing, and using machine learning models to solve business problems. This is a collaborative role, and you will likely work very closely with other entry- to mid-level data scientists and machine learning specialists. Is a machine learning career path right for you? Learn more about this exciting career and its challenges. Opportunities for growth and transition The jump from entry to intermediate-level jobs in data science is relatively short. To reach senior-level roles, you’ll need to: Develop a portfolio of your work and experience. Get more experience in your chosen specialty. Show leadership qualities and strong soft skills (like communication and problem-solving). Regularly upskill to stay on top of the latest developments in the industry. Senior-level roles and responsibilities Your skills and employability will affect how quickly you are promoted to senior-level data science and machine learning roles. Some individuals are promoted in five to seven years, while others might take 20 years or more. These senior roles vary in salary depending on your experience, responsibilities, and who you’re working for, but you can expect $100-135K or more in these senior positions: Senior data scientist A senior data scientist job description comes with increasingly more responsibilities and skill sets. As these are often leadership roles or team leads, your soft skills will play a big role in your promotion to senior data scientist. These soft skills include: Ability to work independently with no oversight or supervision. Advanced problem solving. Ability to lead a team. Proven understanding of project scope and task prioritization. Good communication skills. Mentorship for junior analysts. Advanced knowledge of business practices. You may also expect a less hands-on onboarding process when being promoted or hired as a senior data scientist. You may be required to onboard yourself to quickly understand the business systems and technical architecture already in place. You will be expected to lead and plan more complex projects as a senior team member. For example, you may be asked to coordinate a large-scale data collection and analysis campaign to gather crucial insights for business decisions in the upcoming quarter or manage the end-to-end lifecycle of a project. Your role would likely involve: Model deployment and evaluation. Devising a comprehensive plan and systems architecture. Supporting and mentoring team members. Presenting strategies and results to stakeholders. Does a data science career make sense for you? Learn why data science is a good career. Machine learning engineer If becoming a senior machine learning engineer is in your future, you’ll need advanced leadership and technical skills in this niche area of data science. Your AI and machine learning skills need to be up-to-date and exude the confident decision-making and leadership of someone who’s been in this career for a while. You also need experience implementing Large Learning Models (LLM) and Natural Language Processing (NLP). Those who excel in this advanced career path possess a well-rounded blend of creativity, technical expertise, and problem-solving skills. As a senior machine learning engineer, you will develop and implement machine learning models to address your employer’s or client’s needs. You will drive innovation and efficiency within your IT or business teams, significantly impacting the organization. A machine learning engineer is responsible for: Using deep learning to enhance data search and analysis. Managing data collection, deployment, and monitoring. Using programming languages (including Python, Linux, Postgres, MySql, GCP, AWS, and Docker) to write code for machine learning models. Staying current on the latest strategies, tools, and technologies in data science and machine learning. Meeting industry demand through upskilling and reskilling It’s essential to keep your skills and knowledge up to date in your data science career path. For those already in data science, this means upskilling to gain specialized, current knowledge in this technology field. For those in other IT careers, reskilling can help you gain the necessary edge to be valuable to your current employer (for a promotion) or for a career move to a new company in a more senior or specialized role. Whether you want to become a data scientist, data engineer, or machine learning engineer, start with the Lighthouse Lab’s Data Science Bootcamp. This program can be taken as an immersive, full-time 12-week bootcamp or a flexible, part-time 30-week bootcamp. You’ll graduate with a diploma, but more importantly, you’ll learn all the theoretical, technical, and soft skills you need to progress in your data career. It’s ideal for those already in IT or who have professional experience in another industry and want to branch into data. Check out our Data Science Bootcamp. Data science career FAQs Is data science and machine learning a good career? If you’re good with numbers, a problem solver, and analytically minded, a career in data science or machine learning might be right for you. Both are growing fields, and jobs are expected to grow in the next decade and beyond. Can I do data science and machine learning together? Data science and machine learning go hand in hand. Because machine learning is a branch of data science, you’ll likely learn both in a data science program, such as the Data Science Bootcamp from Lighthouse Labs. Will data science be dead in 10 years? Definitely not! As the world’s need for data grows and computing capabilities and technology improve, more data scientists will be needed to support the collection, organization, and analysis of big and small data. Which is harder: data science or machine learning? It depends on your point of view. Learning data science encompasses many branches of data collection and analysis and requires core transferrable skills like problem-solving and analytical thinking. Machine learning requires the same skills but with specific training in the theory and programming of AI. Which is better: AI and machine learning or data science? Neither is better—they are both related. Data science is the study of data to extract meaningful results, while machine learning requires human data analysts and machine learning engineers to write algorithms to efficiently analyze the data.