How To Get a Data Science Job in 2024

"Data Scientist" is quickly becoming a coveted role. With a high average salary, ample growth opportunities, and flexible work hours, it's no surprise that the Harvard Business Review went so far as to name it the 21st century's sexiest job (yes, this actually happened). But beyond the bells and whistles, there's also a growing need for data scientists. Canada’s overall shortage of talent can be estimated at between 14,000 to 19,000 professionals with deep analytical skills, and 150,000 data literate managers and analysts.

This is where you come in. You might not have the knowledge to become a data scientist yet, but follow the steps below, and you could be well on your way to filling the technical talent gap.

What qualifications do I need to be a data scientist?

Becoming a data scientist requires both technical and soft skills. Known for their sharp mathematical capabilities and finely-tuned problem-solving abilities, data scientists must also be excellent communicators and cross-department collaboration whizzes.

We go into more detail on what skills you need to acquire to land this job, but first, check out the video below to learn more about becoming a data scientist.

1. Education and foundation

The journey to an entry-level data science job begins with a solid educational foundation. Most people pursue a bachelor's or master's degree in computer science, statistics, mathematics, or related disciplines. These programs provide the essential groundwork needed to excel in data science. Focusing on coursework that delves into programming, statistics, linear algebra, and machine learning is crucial.

Another option is taking a Data Science Bootcamp, like that of Lighthouse Labs, where you'll learn the essential data science skills that set you up for long-term success. You'll be surrounded by a dedicated team of knowledgeable instructors, motivated mentors, and a Career Services Team that's achieved an 86% graduation rate for our data science grads.

2. Master data science tools

Proficiency in data science tools is non-negotiable. Familiarize yourself with industry-standard tools and libraries that facilitate data manipulation, cleaning, and visualization. Platforms like Jupyter Notebooks provide an interactive environment for coding and analysis. Libraries such as Pandas and NumPy (Python) or data.table, dplyr, and ggplot2 (R) allow you to manipulate and visualize data efficiently.

3. Learn machine learning

It's essential to gain an in-depth understanding of various machine learning algorithms. Dive into supervised learning methods for tasks like classification and regression. Explore unsupervised learning techniques, including clustering and dimensionality reduction. Develop the skills to evaluate and select the suitable model for a given problem, optimizing its performance.

4. Build a project portfolio

Theory is essential, but practical application showcases your expertise. Engage in personal or collaborative data science projects demonstrating your ability to extract insights from data. These projects hone your skills and serve as tangible evidence of your capabilities. Share your projects on platforms like GitHub, creating an accessible data science portfolio for potential employers to evaluate.

5. Networking and internships

The data science community is rich with opportunities for networking and skill enhancement. Attend local meetups, conferences, and online forums to connect with professionals in the field. These interactions can lead to valuable mentorship, insights into industry trends, and potential job referrals.

Find internships, co-op programs, or volunteer positions that offer hands-on experience. Real-world projects provide a bridge between theoretical knowledge and practical application. Such experiences not only enhance your resume but also offer insights into the day-to-day challenges of a data scientist.

6. Refine communication skills

Strong technical skills are essential, but effective communication sets exceptional data scientists apart. Practice articulating complex data concepts in a clear and understandable manner. This skill is vital for collaborating with cross-functional teams and conveying your findings to stakeholders who may not have a technical background.

7. Job search and applications

When confident in your skills and experience, embark on your job search. Utilize job search platforms, company websites, and professional networks to identify entry-level data science positions. Tailor your resume to highlight your educational achievements, technical skills, relevant projects, and any internships or volunteer work you've undertaken.

8. Prepare and ace interviews

As you secure interviews, preparation becomes paramount. Study common data science interview questions and practice coding exercises. Be ready to explain your problem-solving approach and the rationale behind your decisions. Demonstrate your passion for data science and your ability to thrive in a dynamic, ever-evolving field.

How can I increase my chances of getting a job in data science?

Is it easy to get hired as a data scientist?

Jobs don't grow on trees. You have to make sure you have the skills that make you ultra-employable. Then, of course, there's the question of culture fit, your personal preferences when it comes to your ideal work environment. You'll need to tweak your résumé for each company you apply to and ensure you stand out from the competition in the interview.

These characteristics come standard with any job, but in data science, you can make your profile stand out in many different ways. Check out the video below that discusses the practical steps to land that first data science role.

We've also set out an easy-to-follow list that you can tick as you progress through the steps to up your chances of getting that first data science job.

Advanced specialization

While having basic skills is crucial, consider diving deeper into a specialized area within data science. This could include focusing on specific domains like natural language processing, computer vision, or time series analysis. Specializing can make you stand out as an expert in a particular field.

Expand your knowledge of machine learning algorithms beyond the basics. Learn advanced techniques like deep learning, reinforcement learning, and ensemble methods. Demonstrating expertise in complex algorithms can set you apart from other candidates.

Consider delving deeper into advanced visualization techniques using libraries like Seaborn, Plotly, or Tableau. Being able to present complex insights in visually appealing and interactive formats is a valuable skill.

Kaggle competitions and hackathons

Participate in data science competitions on platforms like Kaggle. This provides a chance to apply your skills to real-world challenges and showcases your problem-solving abilities to potential employers.

Contribute to open source projects

Contribute to relevant open-source data science projects. Your contributions can demonstrate your collaborative skills, commitment to the community, and ability to work on large-scale projects. Participating in projects with other data scientists and tech professionals can also help you develop your leadership and communication skills, which will help you stand out to employers.

Mock interviews and feedback

Practise mock interviews and seek feedback from mentors or peers. Constructive criticism can help you refine your interview skills and presentation.

Consulting and freelancing

Consider offering your data science and programming skills as a consultant or freelancer. This can provide diverse experiences and build your reputation in the field.

Certifications and advanced courses

Consider enrolling in advanced data science certifications or courses offered by reputable institutions. These programs can provide in-depth knowledge and add credibility to your skillset.

Networking with professionals

Continue to expand your network by attending conferences, webinars, and industry events. Networking can lead to mentorship opportunities, job referrals, and deeper insights into the industry.

Tailored job applications

Customize your job applications to match the specific requirements of each position. Highlight relevant skills, projects, and experiences that align with the job description.

What can I expect from an entry-level data science role?

Entry-level data scientists have a promising pathway into the dynamic world of data-driven decision-making. You just might notice that the skills from the above list make their way into day-to-day tasks. As you embark on this journey, here's what you can expect from such a position:

1. Data exploration and cleaning

You'll spend significant time exploring raw datasets, identifying patterns, and cleaning the data to ensure its quality and reliability. This involves dealing with missing values, outliers, and inconsistencies, which are essential steps before analysis.

2. Basic analysis and visualization

In an entry-level role, you'll perform foundational data analysis using statistical methods and data visualization tools. You'll create visual representations to present insights, trends, and correlations in a way that's understandable to both technical and non-technical stakeholders.

3. Feature engineering

Entry-level data scientists often contribute to feature engineering – the process of selecting and transforming variables to improve the performance of machine learning models. This involves understanding the problem, selecting relevant features, and creating new ones that enhance model accuracy.

4. Model building and testing

You'll get hands-on experience building and testing simple machine learning models. While senior data scientists might handle complex models, you'll contribute by applying algorithms, tuning hyperparameters, and evaluating model performance.

5. Collaborative environment

Entry-level roles typically involve working collaboratively with cross-functional teams. You'll interact with domain experts, engineers, and business analysts to understand project requirements, align goals, and translate data insights into actionable recommendations.

6. Learning and upskilling

Entry-level positions are opportunities for continuous learning and growth. You'll have chances to upskill in advanced techniques, tools, and methodologies. Companies often provide resources like workshops, training programs, and access to online courses to support your professional development.

7. Real-world problem solving

Data science roles involve tackling real-world business problems. You'll gain experience defining problem statements, formulating hypotheses, and developing data-driven solutions that impact decision-making and drive business outcomes.

8. Communication skills

Effective communication is vital. You'll develop your ability to convey complex technical findings to non-technical stakeholders. This skill includes crafting clear and concise reports, presentations, and visualizations to ensure your insights are actionable.

While your responsibilities might be more focused and less complex than senior roles, entry-level positions are crucial stepping stones for gaining practical experience, refining your skills, and building a solid foundation for a successful data science career.

Can I get a data science job without experience?

While it is possible to land that first coveted data science role with no experience, it definitely helps to be backed by some level of formal training like Lighthouse Labs' top-tier Data Science Program.

However, the most important step for any aspiring data scientist is putting yourself out there, gaining practical experience, and demonstrating your capabilities. See the video and list below to see how you can go from the ultimate beginner to a data scientist.

Educational foundation

Obtain a degree or complete a bootcamp in a relevant field such as computer science, statistics, or mathematics. Focus on programming, statistics, machine learning, and data analysis coursework. You can also take the self-learning route, which is the most cost-effective, but also requires the most discipline.

Self-fearning and projects

Enhance your skills through online courses and tutorials. Build a portfolio of data science projects showcasing your ability to clean, analyze, and interpret data.

Networking and engagement

Join data science communities, attend meetups, conferences, and webinars to connect with professionals and stay informed about industry trends.

Internships and volunteering

Seek practical experience through internships, co-op programs, or volunteer opportunities. Apply theoretical knowledge to real-world scenarios and build a network within the industry.

Apply for entry-level positions

Tailor your resume and cover letter to highlight your skills, projects, and transferable competencies. Pursue entry-level data science roles or related positions to kickstart your career.

Ready to launch your data science career? Apply to Lighthouse Labs' Data Science Program (it only takes 10 minutes) and put yourself on the path to success.