Find Your First Job As A Data Scientist

The field of data science has become one of the most lucrative and in-demand areas in the job market today. However, finding your first job as a data scientist can be quite challenging, especially if you lack experience or relevant qualifications. In this article, we will discuss the qualifications and technical skills required to get an entry-level job as a data scientist. We will also explore the importance of gaining experience through volunteering and suggest ways to find volunteering opportunities in the UK and USA. Finally, we will offer tips on how to find your first job as a data scientist via an internship and provide links to websites where you can apply.

 

What is a Data Scientist

A data scientist is a professional who analyses and interprets complex data using various statistical, computational, and machine learning techniques. They work to extract insights and knowledge from large and varied datasets to inform business decisions or to solve complex problems in a range of industries.

The role of a data scientist involves collecting, cleaning, and organising large datasets, creating models and algorithms to analyse data, and using data visualisation techniques to communicate findings to stakeholders. They may also be involved in developing predictive models, identifying patterns and trends in data, and recommending actions based on their analyses.

In addition to technical skills, data scientists are often required to have strong communication skills, as they must be able to explain complex findings to non-technical stakeholders. They may also need to collaborate with other professionals such as software developers, business analysts, and project managers to integrate their findings into broader organisational strategies.

Qualifications and Technical Skills

To land an entry-level job as a data scientist, there are several qualifications and technical skills you need to possess. Firstly, a bachelor’s degree in a field related to data science such as computer science, mathematics, or statistics is highly preferred. However, a master’s degree or a Ph.D. in data science, machine learning, or artificial intelligence can also give you an edge.

In addition to a degree, employers seek candidates with technical skills such as proficiency in programming languages like Python, R, and SQL. Familiarity with big data tools such as Hadoop, Spark, and NoSQL databases are also desirable. Moreover, a strong understanding of statistics, data visualisation, and machine learning algorithms is essential.

In addition to technical skills, it is important to have good communication skills and the ability to work in a team environment. Data science projects often involve collaboration with other team members, including business analysts, project managers, and software developers. Therefore, the ability to communicate effectively and work collaboratively is crucial.

Data Science Boot Camps

There are many bootcamps that can help you become a data scientist and prepare you for a job in the field. Here are a few options to consider:

1. DataCamp: DataCamp offers online courses in R and Python programming, data visualisation, machine learning, and more. They offer several career tracks, including a Data Scientist with Python career track, which covers the skills needed for a job in data science.
2. Springboard: Springboard offers a Data Science Career Track that includes mentorship, project-based learning, and personalised career coaching. The program covers topics such as Python programming, statistics, machine learning, and data visualisation.
3. Metis: Metis offers a Data Science Bootcamp that covers topics such as Python programming, machine learning, data visualisation, and big data tools like Hadoop and Spark. They also offer career services to help you find a job in the field.
4. Flatiron School: Flatiron School offers an Online Data Science Bootcamp that covers Python programming, machine learning, data visualisation, and more. The program includes personalised career coaching and job placement support.
5. General Assembly: General Assembly offers a Data Science Immersive program that covers topics such as Python programming, statistics, machine learning, and data visualisation.
6. OpenClassrooms: OpenClassrooms is another popular option that offers a Data Scientist training path, which covers topics such as data wrangling, machine learning, and data visualisation.

It’s important to research and compare different bootcamps to find the one that best fits your needs and career goals. It’s also important to keep in mind that while bootcamps can provide valuable skills and knowledge, they may not guarantee a job in the field. Ultimately, success in finding a job as a data scientist will depend on your skills, experience, and ability to market yourself to potential employers.

 

Gaining Experience by Volunteering

One way to gain experience as a data scientist is to volunteer your skills. Volunteering not only helps you gain practical experience, but it also allows you to build your portfolio and network with professionals in the field. There are several organisations in the UK and USA that offer volunteering opportunities for data scientists.

In the UK, you can volunteer with DataKind UK, a non-profit organisation that collaborates with charities and social enterprises to apply data science to social challenges. Another option is to volunteer with Data Science for Social Good, a program that offers data science fellowships to address social issues.

In the USA, DataKind USA offers similar volunteering opportunities as DataKind UK. Another option is to volunteer with Data Science Corps, a program that provides data science solutions to government agencies, nonprofits, and other organisations.

Getting an Internship in Data Science

Another way to gain experience as a data scientist is to get an internship. Internships are a great way to get hands-on experience in the field and build your professional network. To find data science internships, you can search for job postings on popular job boards such as Indeed, Glassdoor, and LinkedIn. You can also reach out to companies and organisations that you are interested in working for and inquire about internship opportunities.

Finding your first job as a data scientist can be challenging, but internships can be a great way to gain experience and build your skills. Here are some tips to help you find an internship as a data scientist:

1. Look for internships on job search websites such as Indeed, Glassdoor, and LinkedIn. These websites allow you to filter your search by location, job title, and company.
2. Check out the career pages of companies you’re interested in. Many companies post internship opportunities on their career pages.
3. Attend job fairs and networking events. These events provide an opportunity to meet with recruiters and hiring managers, and learn about internship opportunities.
4. Reach out to your professors or alumni network. They may have connections in the industry and be able to help you find an internship.
5. Consider applying to programs such as Google Summer of Code or internships offered by the National Science Foundation.

Here are some websites where you can apply for data science internships:

 

1. Indeed
2. Glassdoor 
3. LinkedIn
4. Google Summer of Code 
5. National Science Foundation
6. InternMatch 
7. Kaggle

In addition, many universities and colleges offer internship programs in data science. For example, Harvard University offers a Data Science Summer Program that provides students with hands-on experience in data science research.

Data Science Hackathons

Another option is to participate in data science competitions and hackathons. These events provide you with the opportunity to work on real-world data science problems and showcase your skills to potential employers. Some popular data science competitions include Kaggle, DataHack, and Analytics Vidhya.

Networking to Help you Find Your First Job as a Data Scientist

Here are some practical networking tips to help you find your first job as a data scientist:

1. Attend industry events and meetups: Attend industry events and meetups where you can meet professionals in the field. This will give you an opportunity to network, learn more about the industry, and potentially find job leads. Register you interest in data science events on  KDnuggets and Eventbrite  joining these events will give you the opportunity to network with data science professionals.
2. Join professional associations: Joining professional associations such as the Data Science Association or the International Association of Computer Science and Information Technology can help you connect with other data scientists and learn about job opportunities.
3. Use LinkedIn: Create a strong LinkedIn profile and start connecting with other data scientists in the field. Join  LinkedIn groups related to data science and participate in discussions to expand your network.
4. Leverage alumni networks: Connect with alumni from your university or college who are working in data science. They may be able to offer advice, referrals, or job leads.
5. Reach out to recruiters: Contact recruiters who specialise in data science roles. They can help connect you with job opportunities and provide insights on what companies are looking for.
6. Participate in online communities: Join online communities such as Kaggle, GitHub, and Stack Overflow to connect with other data scientists and showcase your skills.
7. Attend career fairs: Attend career fairs focused on data science and analytics. This can give you an opportunity to speak directly with hiring managers and learn about available job opportunities.

Remember to be proactive in your job search and make connections wherever possible. By networking effectively, you can increase your chances of finding your first job as a data scientist.

Build a Portfolio

Building a portfolio is an excellent way to showcase your skills and expertise to potential employers, especially when you are seeking your first job as a data scientist. Here are some tips on how to create an effective portfolio:

1. Choose relevant projects: Select projects that demonstrate your ability to solve real-world problems using data science techniques. Look for projects that are relevant to the industry or company you are interested in working for.
2. Show your process: Provide details on the steps you took to complete each project. This includes data cleaning, feature engineering, model selection, and evaluation. This will demonstrate your ability to handle the entire data science workflow.
3. Include visualisations: Use charts, graphs, and other visualisations to help explain your findings. This makes it easier for non-technical people to understand your work and highlights your ability to communicate complex ideas.
4. Explain your results: Provide a summary of your findings and explain what insights you gained from the data. This demonstrates your ability to extract meaningful information from data and apply it to solve business problems.
5. Use open-source tools: Employ open-source tools such as Python and R to showcase your programming skills. This can include libraries like NumPy, Pandas, Scikit-learn, and Tensorflow.
6. Make it easy to read: Keep your portfolio organised and easy to read. Use clear and concise language, and avoid using technical jargon that may be unfamiliar to non-technical people.
7. Include a personal touch: Add a short bio or introduction to your portfolio. This can include your background, interests, and goals as a data scientist. It helps to personalise your work and make a connection with potential employers.

Your portfolio should showcase your skills and demonstrate your ability to solve real-world problems. Keep it relevant, organised, and easy to read, and you’ll be on your way to finding your first job as a data scientist.

What Are The Data Science Entry Level Jobs I Should Apply For?

Here are some of the entry-level data science jobs that are in demand and are a great starting point for a career in data science.

1. Data Analyst – A Data Analyst is responsible for analysing large datasets and providing insights to stakeholders. They should have a strong understanding of statistical analysis, data modeling, and data visualisation tools.
2. Data Engineer – A Data Engineer is responsible for designing, building, and maintaining the infrastructure that supports data analysis. They should have a strong understanding of data warehousing, ETL processes, and big data technologies.
3. Machine Learning Engineer – A Machine Learning Engineer is responsible for developing and deploying machine learning models. They should have a strong understanding of programming languages, such as Python and R, and machine learning frameworks, such as TensorFlow and PyTorch.
4. Business Intelligence Analyst – A Business Intelligence Analyst is responsible for creating reports and dashboards to provide insights into a company’s performance. They should have a strong understanding of data visualisation tools, such as Tableau and PowerBIv.
5. Junior Data Scientist – Junior data scientists are responsible for collecting, analysing, and interpreting data to provide insights to senior-level data scientists. They typically work in industries such as healthcare, finance, or marketing.

Keep in mind that job titles and responsibilities can vary across companies, so it’s important to read the job descriptions carefully and tailor your resume and cover letter accordingly. Also, make sure to acquire the necessary skills and qualifications required for the specific job you’re interested in.

Create a Compelling CV Highlighting Your Skills Related to Data Science

A great entry-level data scientist CV should highlight your skills, education, and experience in data science, as well as any relevant coursework, projects, or internships. Here are some key elements that your CV should include:

1. Summary/Objective statement: This should be a brief statement that highlights your skills and what you can bring to the role.
2. Education: List your degree(s), the institution(s) you attended, and your major(s) and minor(s). Be sure to include any relevant coursework or academic projects that demonstrate your skills in data science.
3. Technical skills: Highlight your proficiency in programming languages such as Python, R, SQL, and any data visualisation tools such as Tableau or Power BI. Also, mention your familiarity with machine learning algorithms and statistical analysis techniques.
4. Experience: Detail any relevant internships or work experience, even if they’re not directly related to data science. Be sure to focus on the skills you developed that are transferable to data science.
5. Projects: List any data science projects you have worked on, including details on the technologies used, the data you analysed, and the results you achieved.
6. Certifications: Include any relevant certifications, such as the Certified Data Scientist or the Google Data Analytics Professional Certificate.

Overall, your CV should showcase your passion for data science, your technical skills, and your ability to work collaboratively and creatively to solve complex problems.

HOW CAN WE HELP?

At Total Career Solutions, we offer the expert, knowledge, experience, and insights and help you change career and find a new career path may be as a data scientist. Book a free 15-minute consultation with our professional career consultants to learn more.