Introduction
There are many positions available that need you to have a background in data science job in USA. Sometimes, it can be perplexing. It is challenging to determine if you are over or underqualified for a position. Companies occasionally have job descriptions that overlap, or even just their own unique concept (and titles) of what responsibilities a position should entail, which doesn't always assist.
In this article we'll try to provide you advice on how to deal with all the many data science job in USA that call for a background in the field. We'll begin by discussing the parallels between the professions as many of these data science positions call for the same or very comparable talents. We'll also go through the credentials and data science expertise you'll need to get a job, as well as sample interview questions. The job description, technical qualifications, and career trajectory will next be discussed in more detail, along with pay.
Background for All Data Science Job in USA
By definition, data science sits at the nexus of multiple fields. It calls for the combination of programming abilities, statistical, mathematics, and/or business domain knowledge. We can determine where data scientists often come from using this definition. They often complete their formal education with a degree in computer science, mathematics, statistics, economics, or another quantitative subject. A degree in the humanities may also be helpful for some data science positions, particularly if the position is primarily focused on human behaviour. Your holding a Master's or even a Ph.D. may be necessary, depending on the seniority of the position.
Skills You Need
Several variables affect it, and there are distinctions between different data science occupations, of course. Nonetheless, there are specific abilities that are necessary for almost every position that calls for a data science background. The only distinction is how much you'll use that talent to your work.
- Collecting, organizing, cleaning, and manipulating data
- Coding is commonly done in SQL, Python, or R, although it may also be done in Java or C++.
- Data visualization, often done using BI tools like Tableau, Power BI, and Looker...
- Understanding how databases operate using database modelling
- Use statistical analysis to acquire insights from data analysis
- Apply your mathematical understanding to data analysis to create metrics.
Career Trajectory
There is no one single path to becoming a data scientist. Your educational background and professional history will determine this. Yet, most people begin their careers as data analysts. After that, individuals often continue in one of two routes depending on their abilities and interests: one is more focused on dealing with data and data infrastructure, while the other is more focused on data analysis. Certain occupations may call for more education, such as a business or humanities degree. You can take any of those pathways to become a data scientist. You have several options for movement; it all depends on your employer, your professional path, your interests, etc.
How Much Can You Earn?
Here is a table of the data science jobs available in the United States. The job title for data scientists and their typical total yearly remuneration are displayed in the table. The occupations are listed in the order of the aforementioned career progression. In this manner, you'll be able to see how your salary may increase if you pursue a conventional path to becoming a data scientist.
| Job title | Average total compensation ($USD) |
| Data analyst | $70k |
| Database administrator | $84k |
| Data modeler | $94k |
| Software engineer | $108k |
| Data engineer | $113k |
| Data architect | $119k |
| Statistician | $89k |
| Business intelligence (BI) developer | $92k |
| Marketing scientist | $94k |
| Business analyst | $77k |
| Quantitative analyst | $112k |
| Data scientist | $139k |
| Computer & information research scientist | $142k |
| Machine learning engineer | $189k |
The General Description of a Data Scientist
A data scientist is a person who employs programming, statistical, and mathematical knowledge to extract meaning from data. Data will be gathered, arranged, cleaned, and analyzed by them. The same applies here for data analysts. They are more prediction-focused and forward-looking, nevertheless. The machine learning models will be constructed using the data. By identifying trends, patterns, and behaviors in the data at hand, they assist them in making predictions. They do it to address business issues and improve the performance of the firm in terms of sales, customer satisfaction, expenses, income, etc. The majority of the abilities you'll need as someone with a background in data science are covered in this career description, which is the most generic one. The other positions listed below are all variations of this one and call for knowledge and abilities in various technical areas of data science.
- Skills Required
1) Programming Languages
- SQL
- R
- Python
- Java/JavaScript
- C/C++/C#
2) Platform Tools
- Platforms for data science and machine learning (e.g., Jupyter Notebooks, MATLAB, KNIME, MS Azure-learning Studio, IBM Watson Machine Learning, etc.)
- BI tools (e.g., Tableau, Power BI, Looker, QlikSense, etc.)
- Recursive databases (e.g., MS SQL Server, PostgreSQL, MySQL, Oracle, HIVE, Snowflake, etc.)
- Online databases (e.g., Amazon Web Service, Microsoft Azure, Google Cloud, etc.)
3) Technical Skills
- Programming
- Data manipulation, analysis, and visualization
- Data modeling
- Model building, testing, and deploying
- Machine learning
- AI
- Cloud computing
- APIs
- Statistics and mathematics
Top Data Science Job in USA
1. Data Analyst
When necessary, this position in data science is required to collect, arrange, and clean data. Following that, they must do routine and one-time analyses and provide findings. By doing so, they contribute to company decision-making and provide solutions to particular business issues. Data visualization and effective communication of analysis findings are typically needed of data analysts. Data scientists use it to make predictions about the future, whereas data analysts use it to describe the past and present. Data analyst technical focus is mainly on Data analysis and reporting.
Additional Skills Required Compared to Data Scientists
- Programming Languages - Similar to a data scientist, but more focused on data analysis, therefore Python is utilised for statistical work and automation.
- Platform Tools - Similar to a data scientist, but employing programming tools like Jupyter notebooks and SQL IDEs more frequently.
- Technical Skills - Analyzing and manipulating data is the same as being a data scientist.
2. Data Engineer
Building and maintaining data infrastructure is the primary responsibility of data engineers. Its goal is to convert data into a "analyzable" format so that data scientists and data analysts may access it. As a result, they must collect, manage, edit, and load data for use by others. Compared to data analysts and data scientists, data engineers are primarily concerned with extracting, transforming, and loading (ETL) data. Data engineer main focus is on Data infrastructure, data cleaning, data preparation and manipulation.
Additional Skills Required Compared to Data Scientists
- Programming Languages - Scala, Go
- Platform Tools - ETL tools (e.g., Microsoft SSIS, XPlenty, Talend, Cognos Data Manager, etc.)
- Technical Skills - ETL
3. Machine Learning Engineer
You must develop, create, and manage artificial intelligence (AI) software and algorithms for this data science position in order to automate prediction models and enable machines to operate without requiring instructions for every operation. To accomplish that, you must arrange and examine the data that will be used to train and validate the machine learning model. This explanation demonstrates that a machine learning engineer is similar to a data scientist, with the exception that they are both concerned with developing and implementing machine learning models. Machine learning engineering main focus is on Model building and deploying to production.
Additional Skills Required Compared to Data Scientists
- Programming Languages - Julia, Scala, Go
- Platform Tools - Application frameworks (e.g., Django, Flask, etc.)
- Technical Skills - Software architecture
4. Research Scientist
Compared to other data science positions we've discussed, this one is more focused on theory and research. Research scientists investigate computer issues and then fix current algorithms or create new ones to address them. They also produce new software, tools, and programming languages that enhance both the functionality and user experience of computers. You'll often be employed in one of three industries: hardware, software, or robots. Research scientist. Research scientists main focus is on Research of computing, user, and business problem. Trying to understand deep-rooted issues and behaviors of users, products, and features.
Additional Skills Required Compared to Data Scientists
- Programming Languages - Deep knowledge of programming theory and principles
- Platform Tools - No specific tools required due to the theoretical nature of the job
- Technical Skills - Hardware engineering, Software architecture
5. Marketing Scientist
The individual with this data science job title examines marketing data in a methodical manner. By appropriately evaluating the data and identifying a common pattern that indicates client behaviour, you will be supporting the decision-making process. You'll conduct experiments to support or refute the hypotheses in order to do that. In essence, this is the same as a data scientist, except you work with marketing-related data instead, such email engagement statistics. Marketing scientist main focus is on Data science applied to marketing and sales data, solving business problems related to marketing and sales (for example, field force sizing and marketing ROI).
Additional Skills Required Compared to Data Scientists
- Programming Languages - similar to a data scientist, but with a focus on data querying using SQL and statistical and economic modelling using Python/R.
- Platform Tools - Similar to a data scientist, but with a focus on marketing data and using systems like Google Analytics or Heap Analytics.
- Technical Skills - Marketing and business knowledge
6. Business Intelligence (BI) Developer
The engineer who creates and maintains BI interfaces and works with BI tools is a data-savvy BI developer. These are technologies that enable data visualisation and querying, dashboard creation, and regular and ad-hoc report generation. In some ways, this is a mix of a software engineer, data analyst, and data engineer (ETL) (software development). Business intelligence main focus is on Building graphical dashboards.
Additional Skills Required Compared to Data Scientists
- Programming Languages - Similar to a data scientist, but with a specialization on data-querying; for more advanced applications and statistical modelling, Python and R are employed instead of SQL.
- Platform Tools - The same as a data scientist, but with a stronger focus on business intelligence (dash boarding tools such as Tableau)
- Technical Skills - ETL/ELT, Data warehousing, Software development, Business background.
7. Business Analyst
The systems and procedures used by the firm are evaluated by this data science position. They examine them and provide answers, frequently in the form of new or improved systems and other technical advancements. This should result in better decision-making and cost reductions for the business, which should increase revenue. Business analyst similar to a data analyst, but can also be focused on internal reporting like finance and improving the company’s systems and processes.
Additional Skills Required Compared to Data Scientists
- Programming Languages - Generally only SQL
- Platform Tools - Business analysis tools (e.g. Modern Requirements, Axure, Enterprise Architecture, etc.)
- Technical Skills - Project management, Software testing, Business background
8. Data Modeler
Their responsibility is to create, manage, and update data models, which they subsequently use to develop databases. They do it to enhance database performance generally and data accessibility in particular. They must work together with data architects and administrators to do it. Data modeler main focus is on Data modeling and database design.
Additional Skills Required Compared to Data Scientists
- Programming Languages - Generally only SQL
- Platform Tools - Data modeling (e.g., DbSchema, ER/Studio, Draw.io, etc.)
- Technical Skills - Database design, Data warehousing, ETL/ELT
9. Database Administrator
Database management is the responsibility of this data science position. This implies that they collaborate with data modelers and data architects while implementing databases. Only that they give less attention to philosophical difficulties and more to practical and technological ones. Their responsibility is to ensure that databases are accessible, which includes granting (or denying) access, backing up and restoring data, guaranteeing data security and integrity, and maintaining excellent database performance. Database administrator main focus is on Database administration and maintenance.
Additional Skills Required Compared to Data Scientists
- Programming Languages - Generally only SQL
- Platform Tools - Database administration (e.g., PGAdmin4, SQL Server Management Studio, phpMyAdmin, etc.)
- Technical Skills - Database design, Data warehousing, ETL/ELT, Database administration
10. Data Architect
The data architect position is a high-level one in the data science field compared to data modeler and database administrator. The data architect's role is to build the entire data management architecture while keeping in mind the business requirements of the enterprise. In addition to databases, this also entails developing the architecture for how data will be gathered, used, modelled, retrieved, and secured. Generally speaking, this entails offering an architecture that will be present from the point at which data enters the business to the point at which it departs. Data architect main focus is on Architecture and infrastructure of data management.
Additional Skills Required Compared to Data Scientists
- Programming Languages - Similar to a data scientist, but with a concentration on databases and data, they generally utilize SQL, with Python and Java being used for application development as necessary.
- Platform Tools - Database administration (e.g., PGAdmin4, SQL Server Management Studio, phpMyAdmin, etc.), Big data (Apache Hadoop, Cassandra, MongoDB, etc.), Data modeling (e.g., DbSchema, ER/Studio, Draw.io, etc.)
- Technical Skills - Database design, Data warehousing, ETL/ELT, Database administration
11. Software Engineer
The title of this data science position is comparable to that of a data engineer. The key distinction is that, unlike data engineers, they are typically not interested in data infrastructure. Instead, they create software on top of this data architecture that enables end users to access and utilize the underlying data. Software engineer main focus is on Software development.
Additional Skills Required Compared to Data Scientists
- Programming Languages - Scala
- Platform Tools - DevOps (e.g., Docker, Kubernetes, etc.), Continuous integration/continuous delivery (CI/CD) (e.g. Jenkins, CircleCI, Bamboo, GitLab, etc.)
- Technical Skills - Software architecture, developing, and testing, Database design, Data warehousing, ETL/ELT, Database administration
12. Statistician
This position has a similar title to that of a data scientist. The distinction is that it just addresses the statistics portion of the data scientist's role. They also do data analysis, use statistical techniques to the data, and spot patterns and trends that can aid in decision-making and offer corporate insight. Statistician main focus is on Statistical analysis of data.
Additional Skills Required Compared to Data Scientists
- Programming Languages - Similar to a data scientist, but more focused on statistics and data analysis (many more R users in this field, but Python is also popular)
- Platform Tools - The same as a data scientist, but with more emphasis on statistical analysis software (e.g., SPSS, MATLAB, SAS)
- Technical Skills - Similar to a data scientist, but more focused on statistics and data analysis.
13. Quantitative Analyst
With a concentration on financial data, this position is essentially the same as a data scientist's. To assist the business comprehend financial markets and their tendencies, quantitative analysts, sometimes known as "quants," will examine data and create models. The corporation will make decisions regarding its investments, FX and equities trading, loan approvals, etc. based on such analysis and models. Quantitative analyst main focus is on financial data.
Additional Skills Required Compared to Data Scientists
- Programming Languages - the same as a data scientist, but with a concentration on Python/R for developing quant models
- Platform Tools - Automated trading platforms (MetaTrader4, eToro, etc.)
- Technical Skills - Financial mathematics, Risk management
Conclusion
Data science jobs in the USA are in high demand due to the increasing importance of data in modern business operations. The average salary for a data scientist in the USA is also relatively high, making it a lucrative career option for those with the right skills and experience. However, competition for data science jobs is also high, and candidates will need to possess a combination of technical, analytical, and communication skills to succeed. Top Universities in USA like Columbia University, Stanford University, University of Pennsylvania and more offer Masters in Data Science.Overall, data science is a promising career option in the USA for those with the right skills and experience, and the demand for data scientists is only expected to increase in the years to come.
