Data scientists study and solve complex problems using data in myriad industries. They formulate and design new approaches to technology and information systems.
Sample job description
Do you have an eye for detail? Can you pluck out useful and relevant details from all the background noise? If so, we’re looking for you! [Your Company Name] is looking to hire an experienced data scientist to collect and sift through huge pools of data for our company. You will design and implement data collection systems, create tools to harvest and automate data collection, transform data into useful reports and presentations, and finally create actionable results with the information. The ideal candidate should have excellent mathematical and statistician skills, along with strong technical understanding of software and data structures. Being fluent in several programming languages, understanding software and storage architecture, and having the keen eye to detect patterns and correlations is vital to this highly lucrative position.
Typical duties and responsibilities
- Develop and improve software systems
- Design experiments to test system operations
- Analyze results
- Perform data mining to meet business objectives
- Research, develop, and implement innovative techniques for the organization’s data
- Work with large and complex sets of data
Education and experience
This position requires a bachelor’s degree in computer science, information technology, applied mathematics, or a related field. Many employers prefer candidates who have a master’s or doctorate.
Required skills and qualifications
- Strong aptitude for business, technology, mathematics, and statistics
- Solid written and verbal communication skills
- Ability to work with a team to examine and solve complex issues
- Solid understanding of computer programming and language
Preferred qualifications
- 3+ years experience in ETL and/or data wrangling techniques
- Fluent in SQL syntax
- 3+ years experience in Statistical/ML techniques
- Strong knowledge of best data practices
Average salary and compensation
The average salary for a data Scientist is $137,200 in the United States. Position salary will vary based on experience, education, company size, industry, and market.
| Location | Salary Low | Salary High |
|---|---|---|
| Phoenix, Arizona | $136,450 | $184,600 |
| Los Angeles, California | $153,950 | $208,250 |
| Denver, Colorado | $128,250 | $173,550 |
| Washington, DC | $156,250 | $211,450 |
| Miami, Florida | $127,650 | $172,750 |
| Orlando, Florida | $117,750 | $159,350 |
| Tampa, Florida | $118,950 | $160,950 |
| Atlanta, Georgia | $124,750 | $168,850 |
| Chicago, Illinois | $143,450 | $194,050 |
| Boston, Massachusetts | $155,150 | $209,850 |
| Minneapolis-St. Paul, Minnesota | $123,650 | $167,250 |
| New York City, New York | $163,250 | $220,850 |
| Philadelphia, Pennsylvania | $132,950 | $179,850 |
| Dallas, Texas | $129,450 | $175,150 |
| Houston, Texas | $128,850 | $174,350 |
| Seattle, Washington | $149,250 | $201,950 |
| National Average | $116,650 | $157,750 |
Typical work environment
The vast majority of a day in the life of a data scientist will be spent seated at a computer. Depending on the size of the company or project, this may be a team environment, however not always.
Additionally, this position can be done remotely, creating the opportunity for a more flexible work environment, and a position that can be done freelance.
Typical hours
Typical hours for this position are from 9 AM to 5 PM, Monday through Friday, in an office setting.
Available certifications
As data scientists work in a variety of industries, there are many institutions that offer certifications, including:
- Certified Analytics Professional. By completing and obtaining this certification, you will have proven yourself more than capable of transforming highly complex and voluminous amounts of data into coherent points with logical conclusions. You’ll walk away with a deeper level of insight that allows you to competently explain your points to non-technical stakeholders and bring value to your work and your company.
- Cloudera Certified Associate. This certification training will teach or reinforce your understanding of SQL development, which is the backbone of a huge swathe of report creation, modeling, and management systems. By carrying this certification, you give employers confidence that, if necessary, you could develop, create, and implement a system that will provide value for yourself.
- Cloudera Certified Professional Data Engineer. This certification demonstrates the holder’s ability to carry out data engineering tasks with a high level of mastery. Common and expected data science skills are rigorously tested, and stands as a performance-based test to show the holder has hands-on experience and a stress-tested understanding of best practices and methodologies.
Career path
This position requires a bachelor’s degree in a computer-related field. Employers may prefer candidates who have a master’s or doctorate. Data scientists have the opportunity to advance to positions such as senior data scientist/analyst and senior big data scientist/analyst. Opportunities for advancement vary depending on the industry.
US, Bureau of Labor Statistics’ job outlook
SOC Code: 15-1221
| 2020 Employment | 33,000 |
| Projected Employment in 2030 | 41,700 |
| Projected 2020-2030 Percentage Shift | 22% increase |
| Projected 2020-2030 Numeric Shift | 7,200 increase |
Position trends
According to industry website Computer Science Online, big data is now a major component of the infrastructure, products, and services of organizations. Big data should be a $125 billion industry by the International Data Corporation, introducing a plethora of innovative processes, terms, and technology. This trend necessitates data management and interpretation skills, significantly increasing the demand for data scientists.
Sample interview questions
- Can you explain linear regression?
- Can you explain logistic regression?
- Can you define a confusion matrix?
- What’s the difference between true positive and false positive rates?
- What do you consider to be the key differences between data science and traditional application programming?
- What’s the difference between supervised and unsupervised learning?
- What are popular libraries used in data science?
- How would you go about pruning in a decision tree algorithm?
- What’s an RNN?
- What’s an ROC curve?
- How would you explain the difference between data modeling and database design?
