Want to launch your career in data science but don’t know where to start?
Check out this step-by-step guide to landing your first data science job. If you’re entering the field without any formal training or professional experience, consider building the skills you need to get hired through a high-efficacy bootcamp program Springboard’s Data Science Career Track. If you don’t find a data science job within six months of graduating, you’ll get your money back.
For those starting a data science career from scratch, here’s where to begin.
Cultivate Technical Expertise
You’ll need a combination of theoretical knowledge and applied skills to land your first data science job. Self-learning and massive open online courses can expand your familiarity with core data science concepts, but the best bootcamp programs offer hands-on curricula that allow students to learn through working on realistic data science projects.
To get hired as a data scientist, you’ll need to learn how to identify business problems, collect, wrangle, and analyze relevant data, use machine learning to make predictions, and deliver impactful business insights via data-driven storytelling. You’ll also need to learn how to leverage an ecosystem of Python-based tools and libraries.
Acquire Hands-On Experience
If you need experience solving real-world data science problems outside of an educational context, start with GitHub. An active GitHub profile can attract the attention of recruiters, particularly if you have a strong repository. Open source contributions will attest to your technical skills, and you can also use GitHub to analyze real-world data that you find compelling.
You can also acquire hands-on experience by participating in data science competitions hosted by platforms like Kaggle, DrivenData, CrowdANALYTIX, Innocentive, and more. Many of these platforms are dedicated to using data science to solve unique types of real-world issues. DrivenData’s competitions, for example, focus on applying data science to social problems in health, education, public services, and more.
Craft A Killer Portfolio
A robust portfolio will inspire confidence in your technical skills, creativity, analytical prowess, and ability to collaborate. To establish credibility with hiring managers, be sure to assemble a portfolio of projects that speak to your ability to use real data. Hiring managers are also particularly intrigued by projects that extract novel insights from interesting public data sets. Look at publicly available data from city governments, universities, and more for inspiration.
A strong portfolio also highlights a dynamic skillset. You’ll want to include a data cleaning project, a data storytelling project, and a machine learning project to attest to the breadth and depth of your abilities. Thorough, concise documentation on GitHub or a personal blog should show your code and contextualize the project. This will also help HR professionals and other non-technical decision-makers to understand your work when reviewing your application.
Work Your Network
To maximize your chances of winning a job referral, spend time networking both digitally and in person. Market your skills by building a blog through which to share your projects and documentation. Stay active on social media, where you can interact with other data scientists and promote your blog or GitHub profile. Connect with others in the field on LinkedIn, and be sure to use relevant keywords in your profile so that recruiters can find you through LinkedIn’s search tool.
To network in person, try joining data science Meetup groups in your area. KDnuggets and Eventbrite also publicize data science events. Data science conferences like Strata and KDD will introduce you to other data professionals as well as emerging technologies. Last but not least, building a relationship with a data science mentor will open up a vast network of seasoned professionals. Your mentor can also help you design a career strategy and navigate the industry.
Apply, Apply, Apply
In addition to scouring major job boards like Indeed and Glassdoor, check smaller boards that have higher response rates ‘AngelList, HackerNews, and Hired are just a few examples. Next, attempt to connect with technical recruiters or data scientists at companies that interest you. To initiate a conversation, craft a concise pitch email expressing your interest in their work and how your skills and experiences align with their mission.
Finally, build relationships with others in the tech industry to increase the likelihood of a referral. Ask people who you want to get to know to join you for coffee and talk more about what they do. Show an interest in their work, and be sure to maintain a relationship afterwards by following up and connecting online. The best way to get a referral is to ask for one from someone you have a relationship with.
Prepare to Interview
Every data science interview is different. Some interviews focus on product and metrics, while others focus on a combination of machine learning and programming. Netflix data science interviews, for example, focus heavily on A/B testing, metric design, and product sense, but Microsoft data science interviews focus on programming, SQL, and machine learning. You can determine which topics to focus on by analyzing the job description and asking for clarification from the hiring manager.
Data science interview questions might cover: coding, modelling, algorithms, statistics, probability, product, business case studies, system design, and technical concepts. You should also be able to demonstrate familiarity with cloud-based platforms and data visualization tools.
Ace Your Technical Eval
Your technical evaluation will test your applied skills, problem-solving abilities, and capacity to communicate. It will also offer a window into your process and give hiring managers an idea of what it might be like to work with you. Be sure to ask clarifying questions when necessary, practice your presentation if a presentation is required, and turn in your deliverables either early or on time.
To prepare, try your hand at algorithm and data structure problems on LeetCode, and SQL problems on StrataScratch. Additionally, dedicate some time to research the company’s product and reflect on what metrics might determine the product’s success.
Close the Deal
Congratulations ‘you’ve landed your first data science job offer! Before accepting, consider trying to negotiate your salary. Be sure to consult your mentor or research average salaries for data scientists in comparable roles in your area and negotiate up if necessary. If you have more than one offer, competing offers can be used as leverage to secure higher pay, better benefits, or remote options. You’ve worked hard to get here, so make sure your compensation matches your qualifications!
Ready to land your dream role?
If you’re ready to embark on your data science journey, Springboard’s Data Science Career Track can help. With 14 real-world projects and unlimited one-on-one mentor support, you’ll build the portfolio and professional network you need to land your first job in data science.