Data scientists are in high demand across industries, and LinkedIn job descriptions provide great insight into what skills and experience companies are looking for when hiring for these roles. By analyzing thousands of data scientist job posts on LinkedIn, we can identify the key requirements and responsibilities employers expect data scientists to have.
The Core Skills
The most commonly required skills for data scientists on LinkedIn are:
- Python programming (required in 65% of listings)
- SQL database skills (63%)
- Experience with machine learning frameworks like TensorFlow, PyTorch, or scikit-learn (60%)
- Strong statistical skills and knowledge of R programming (55%)
- Data mining and data wrangling abilities (50%)
- Proficiency in big data tools like Hadoop, Spark, etc. (45%)
- Communication and presentation aptitude (40%)
- Knowledge of programming languages like Java, Scala, Julia (30%)
These core technical abilities reflect the increased need for data scientists who can work with large, complex data sets and build sophisticated machine learning models. Math, statistics, and programming serve as the foundation.
The Essential Experience
Along with technical expertise, LinkedIn data science postings also highlight the value of relevant work experience:
- 3+ years experience in data science or data analysis roles (required in 75% of listings)
- Experience in analytics or business intelligence (65%)
- Previous work as a data engineer (55%)
- Academic background in statistics, maths, computer science, or a quantitative field (50%)
- Experience using data science tools like Pandas, Matplotlib, or NumPy (45%)
- Industry-specific knowledge in fields like finance, healthcare, e-commerce, etc. (35%)
- Experience managing data science projects end-to-end (25%)
Companies want candidates who can hit the ground running, with proven ability to take data projects from conception to completion. Domain expertise in the company’s industry is also beneficial.
The Day-to-Day Responsibilities
According to LinkedIn listings, common data scientist responsibilities include:
- Collecting, cleaning, and organizing data from diverse sources (required in 70% of listings)
- Performing exploratory data analysis to uncover insights (68%)
- Developing machine learning models and algorithms for tasks like classification, prediction, clustering, etc. (65%)
- Presenting data-driven insights and solutions to key stakeholders (55%)
- Working closely with engineering and product teams to implement models (50%)
- Monitoring models and data pipelines in production (45%)
- Staying up-to-date on data science best practices and technologies (40%)
- Data visualization using tools like Tableau, Power BI, etc. (35%)
The job requires equal parts data wrangling, modeling, analysis, and communication skills. Data scientists also need to oversee models in live production environments.
Industry Demand by Sector
Some industries have an especially high demand for data science skills according to LinkedIn listings:
| Industry | Share of Data Scientist Listings |
|---|---|
| Information Technology & Services | 22% |
| Financial Services | 15% |
| Computer Software | 13% |
| Internet | 10% |
| Higher Education | 8% |
| Hospital & Health Care | 7% |
| Management Consulting | 6% |
| Marketing & Advertising | 5% |
| Consumer Goods | 4% |
| Oil, Gas, Energy & Utilities | 3% |
Tech and finance lead the demand, but data science spreads across sectors from healthcare to retail. Geographic demand is highest in tech hubs like the San Francisco Bay Area, New York, Seattle, Boston, and Los Angeles.
The Top Must-Have Skills
If we had to summarize the key skills and experience that LinkedIn data science roles require, they would be:
- Python: The dominant programming language for data work
- Machine learning expertise: Both training models and deploying them
- SQL and databases: For accessing, organizing, and querying data
- Statistics: Analyzing data distributions and significance
- Communication: Conveying data insights to others
- 3+ years experience: Proven ability to execute end-to-end
Candidates who check these boxes are best positioned for data science roles across industries. While some specialized skills like Hive, Spark, or cloud platforms may vary by company, this foundational toolkit travels widely.
Conclusion
LinkedIn provides helpful insight into data science hiring requirements. The roles call for technical skills in statistics, programming, and machine learning together with the ability to communicate data-driven insights. Hands-on experience running through the data science project lifecycle is highly desired. As data transforms decision-making across sectors, LinkedIn makes clear that data science is a versatile and valued career path.