With the rise of big data and artificial intelligence, the role of data scientist has become one of the most in-demand jobs in recent years. However, breaking into the field is not always easy. In this article, we’ll examine what it takes to get hired as a data scientist, including the skills and experience required, how to build up your resume, and tips for acing the interview process.
What is a data scientist?
A data scientist is a professional who utilizes statistical and analytical methods to extract insights and information from large data sets. Their primary goal is to solve complex business problems through data analysis. Common responsibilities of a data scientist include:
- Collecting and cleaning large, structured and unstructured data sets
- Performing exploratory data analysis to uncover patterns, trends and relationships
- Developing and optimizing data mining models and algorithms
- Running simulations and scenario analysis to test hypotheses and predict outcomes
- Interpreting and visualizing analytical results using statistical models and data visualization tools
- Communicating data insights to key stakeholders to influence business decisions
In summary, data scientists utilize their math, statistics, and programming skills to extract meaningful insights from complex data. They combine domain expertise, analytics, and storytelling to solve real-world problems that impact the business.
What skills and background are required?
To be an attractive data science candidate, you’ll need a solid educational background along with specialized technical skills:
Educational Background
- A bachelor’s degree in a quantitative field like math, statistics, computer science, physics, or economics is preferred
- An advanced degree like a master’s or Ph.D. is often required, especially for more senior roles
- Relevant coursework in statistics, data mining, machine learning, and database management
Technical Skills
- Programming: Proficiency in languages like Python, R, SQL, Scala, or Java
- Math/statistics: Statistical modeling, predictive modeling, regression, calculus, linear algebra
- Machine learning: Popular techniques like regression, classification, clustering, neural networks
- Data visualization: Using tools like Tableau, Power BI, ggplot2 to communicate insights
- Big data: Experience with distributed systems like Hadoop, Spark, Hive, Pig
- Databases: Knowledge of SQL and experience with relational databases
Besides the technical expertise, data scientists need curiosity, analytical thinking, business acumen and communication skills to succeed in the role.
How much experience is needed?
The amount of experience required depends on the level of the data scientist role:
Entry-Level (0-3 years experience)
- Internships, bootcamps, or course-based projects
- Junior or associate data scientist roles
- Focus on developing core programming and data skills
Mid-Level (3-6 years experience)
- At least 2-3 years industry experience as a data scientist
- Increasingly independent work and project leadership
- Deep knowledge of advanced analytics and modeling techniques
Senior-Level (6+ years experience)
- 5+ years experience applying data science across multiple roles/companies
- Technical expertise plus project leadership and stakeholder management
- Strategic mindset, with ability to identify and solve high-impact business problems
The more experience you gain as a data scientist, the more opportunities open up at the mid-senior levels. But you can break into entry-level roles through self-learning, certifications and internships.
How can you build up your resume?
Here are some tips to build up your resume and gain the right skills and experience to be an attractive data science candidate:
- Pursue higher education in a quantitative field like statistics, computer science or applied math
- Complete online courses and certifications in data science, machine learning and programming
- Build a portfolio of data science projects to showcase analytical, technical and storytelling skills
- Gain hands-on experience through internships, freelancing or volunteer data projects
- Learn business domain knowledge and software tools used in the industry
- Network and make connections with data scientists and recruiters at industry events
Some key platforms to gain experience and build your portfolio include:
- Kaggle – Enter data science competitions and work on datasets
- GitHub – Build a profile showcasing data science projects and code
- Tableau Public – Create interactive data visualizations to share
- Medium – Write blog posts to demonstrate communication skills
How can you prepare for data science interviews?
The data science interview process typically involves both technical and non-technical interviews assessing your hard and soft skills:
Technical interview prep
- Review machine learning algorithms, statistics, programming languages and databases
- Practice discussing and whiteboarding data science solutions and architectures
- Work through technical questions on platforms like LeetCode and HackerRank
- Consider doing mock interviews to get feedback on your hard skills
Non-technical interview prep
- Review your resume and data science projects to discuss in detail
- Prepare stories that highlight your analytical skills, leadership and business acumen
- Practice talking through data science case studies and presentation scenarios
- Research the company’s industry, business model and competitors
Come prepared to discuss technical projects in plain business language. Soft skills and cultural fit also play a major role in data science hiring, so showcase your curiosity, communication abilities and collaborative spirit.
What industries employ data scientists?
Some top industries hiring data science talent include:
Technology
- Software and internet companies like Google, Facebook, Microsoft, LinkedIn
- Leveraging data science for recommendation systems, ad targeting, fraud detection, search optimization
Finance and Banking
- Banks, hedge funds, financial institutions
- Applications in trading, portfolio management, risk modeling, pricing algorithms
Healthcare
- Hospital systems, biotech, pharmaceutical companies
- Analyzing clinical trial data, modeling disease progression, improving patient care
Retail and eCommerce
- Online and brick-and-mortar retailers
- Personalization, customer segmentation, inventory and price optimization
Media and Entertainment
- Gaming companies, streaming platforms like Netflix/Spotify
- Recommender systems, ad targeting, predictive analytics on user behavior
Data science roles can be found across many industries. Consider your interests and target roles where you can apply analytics to high-impact business problems.
What is the average data scientist salary?
According to Glassdoor, the average annual salary for a data scientist is $117,288 in the United States, as of October 2022. However, salaries can vary significantly based on factors like location, years of experience and industry:
| Years of Experience | Average Base Salary |
|---|---|
| Entry Level (0-3 years) | $95,000 – $115,000 |
| Mid-Level (3-6 years) | $115,000 – $135,000 |
| Senior Level (6+ years) | $135,000 – $165,000 |
Higher salaries are common in major tech hubs like Silicon Valley and New York City. Industries like finance, software and pharmaceuticals also tend to pay above average salaries for data science roles.
What are the top skills data scientists need to know?
Here are some of the most important skills for data scientists today:
Python programming
Python is the most popular programming language for data science work due to its versatility and extensive ecosystem of data libraries.
SQL + Databases
SQL skills are essential for extracting and manipulating data from databases, the core starting point for analysis.
Machine learning
Algorithms like regression, classification and clustering are used to train predictive models on data.
Statistics
Statistical fundamentals are key for designing experiments, analyzing results and quantifying reliability.
Data visualization
Visualization tools like Tableau turn data into visual stories and dashboards to uncover insights.
Big data frameworks
Tools like Hadoop, Spark and cloud platforms enable handling and analysis of large data at scale.
Communication skills
Data scientists need to clearly explain analytical findings and recommendations to stakeholders.
While the core skills involve statistics, programming and machine learning, data scientists also need business acumen, curiosity and creativity to succeed.
Should you become a data scientist?
Before pursuing a data science career, ask yourself:
- Are you excited by the prospect of uncovering insights from complex data?
- Do you enjoy statistics, programming, visualization and modeling?
- Are you curious to learn about new techniques like machine learning and AI?
- Do you like communicating insights through visuals and storytelling?
- Are you comfortable working with ambiguity and complex problem solving?
If you answered yes to most of these questions, you likely have the analytical bend of mind and curiosity required to thrive as a data scientist. Some other traits that lead to success are:
- Critical thinking abilities
- Creativity and a willingness to experiment
- Business acumen and strategic mindset
- Teamwork and communication skills
Look at the day-to-day responsibilities of data scientists and see if you find them intrinsically rewarding. The role requires a blend of hard and soft skills. Assess your strengths and motivations to determine if data science is the right career path for you.
How can you break into data science?
Here is a step-by-step approach to breaking into the data science field as a beginner:
- Learn fundamental programming skills: Start with Python basics, SQL, data structures, algorithms.
- Explore core data science topics: Statistics, machine learning, data visualization, databases.
- Build portfolio projects: Analyze datasets, work on end-to-end modeling projects, create visualizations to showcase skills.
- Consider a related role: Data analyst, business analyst or BI analyst roles to gain initial experience.
- Complete online courses/certifications: Coursera, edX, Udacity, DataCamp and other platforms offer specialized data science programs.
- Earn a master’s degree: Many transition into data science through a master’s program in the field.
- Apply for entry-level jobs: Data scientist, data science consultant, quantitative analyst, statistical analyst.
- Grow your skills/network: Attend meetups, conferences, engage with the data science community.
With some dedicated self-study time and passion for working with data, you can gain the skills needed to land your first data science role. Be persistent when job hunting and be willing to start in related roles to get your foot in the door.
What is the future outlook for data science jobs?
The future continues to look very bright for data science roles. According to IBM, demand for data scientists is projected to grow 28% by 2020. Other estimates predict dramatic growth as well:
- 11.5 million job openings for data science roles by 2026 (LinkedIn)
- Over 700,000 data scientist job listings posted in 2020 (Dice Insights)
Data science is the fastest growing profession in tech. As organizations collect more data and seek to extract insights from it, hiring for roles like data scientists, data engineers and data analysts will continue to surge.
Some key drivers of demand include:
- Growth of big data and availability of massive datasets
- Adoption of machine learning and AI across industries
- Focus on data-driven decision making and predictive analytics
- Emergence of specialized data tools and infrastructure
There is especially high demand for candidates with strong machine learning, artificial intelligence and cloud platform skills. Data science professionals who stay up-to-date with the latest technologies will be well-positioned to advance their careers.
Conclusion
Becoming a data scientist requires dedication, but offers immense opportunities for growth. Gaining the right mix of technical abilities, business acumen and communication skills is key to breaking into the field. While the barriers to entry are high, they can be overcome through persistence. The demand for data-driven talent will continue to rise in the future. Candidates who invest time to build up their skills and experience will find rewarding, in-demand careers in data science.