Big data means big job opportunities these days if you have, or are in the process of acquiring, data science skills. Because data is now the collective currency of influence among business leaders, technologists and consumers, it pays to know how to collect, clean, sort and analyze data. Whether you pursue a data scientist career or work in any of the related fields, it's worth considering what the expectations are for the role.
Here, we offer the typical job requirements, average salary, formal education required and experience needed for seven different jobs in data science: data scientist, senior data scientist, data analyst, data engineer, business intelligence analyst, machine learning engineer and business analyst.
Typical job requirements: A data scientist collects, analyzes, cleans and interprets extremely large amounts of data. Data science teams use advanced analytics technologies, including machine learning and predictive modeling. Basic responsibilities include gathering and analyzing data, using various types of analytics and reporting tools to detect patterns, trends and relationships in data sets.
Average salary: $113,309 base salary, according to Glassdoor (Dec. 2020)
Formal education required: While some job listings place the minimum education requirement at the bachelor's level, most data scientist jobs require a minimum of a master's degree in engineering, computer science, mathematics, computational statistics, operations research, machine learning or related technical fields. Some jobs require a Ph.D. in quantitative fields, such biostatistics, mathematics or computer science.
Experience needed: Experience as a data analyst is excellent preparation for success as a data scientist. Knowledge of R, Python, statistics, data analytics, data visualization and machine learning is needed. Data science requires knowledge of a number of big data platforms and tools, including Hadoop, Pig, Hive, Spark and MapReduce. Knowledge is also needed in programming languages such as SQL, Python, Scala and Perl.
Hard skills required for the job include data mining, machine learning, deep learning and the ability to integrate structured and unstructured data. Experience with statistical research techniques, such as modeling, clustering, data visualization and segmentation, and predictive analysis are also a big part of the roles. At a minimum, many jobs require two or more years of experience in a business environment as a data scientist, business analyst or quantitative analyst. Similarly, many positions assume three to five years of experience working with advanced machine learning techniques, probability theory, Bayesian analysis or causal inference methods.
Senior Data Scientist
Typical job requirements: What sets senior data scientists apart from their junior colleagues is the proven ability to transform data into actionable solutions to drive business. The senior data scientist takes initiative in the development of advanced analytical models to solve problems and pursue new business opportunities, working closely with business stakeholders.
Formal education required: Minimum of a master's degree in computer science or related degree. Some jobs require a Ph.D. in quantitative fields, such biostatistics, mathematics or computer science.
Average salary: $134,222 base Glassdoor (Dec. 2020)
Experience needed: The experience required varies to some degree by the employer's industry. But typical job descriptions at this level require experience with several of the following:
- Advanced machine learning algorithms
- Regression analysis, machine learning, algorithms and data structures
- Python, using NumPy, Pandas, SciPy and similar packages
- Structured prediction models
Typical job requirements: Data analysts are often closer to the day-to-day business than data scientists. Both are data science experts in their own right. They work with algorithms and models that focus on specific business issue or have a customer focus. In contrast, data scientists steer the business with insights for the long term, as described above.
Formal education required: Bachelor's or master's degree required, with background in statistics, computer science, operations research or similar fields.
Average salary: $62,453 base Glassdoor (Dec. 2020)
Experience needed: Many employers expect that you have worked in some capacity with business intelligence. Beyond that, you're expected to have a strong background in:
- Relational databases and SQL
- Marketing analytics concepts, such as LTV modelling and ROI analysis
- Extract, load, transform (ELT) data integration processes, such as Pentaho or Talend
You'll do well is you also have hands-on experience with advanced statistical concepts, such as cluster analysis, singular value decomposition, stochastic gradient descent and Bayesian methods.
Typical job requirements: Data engineers prepare data for analytical use, build data pipelines and consolidate and clean data. The role involves developing applications that can accumulate and derive meaning from large data sets. Doing so requires working with a broad spectrum of stakeholders, including non-technical business and product colleagues. Because data engineers deal with both structured and unstructured data sets, they must be versed in different approaches to data architecture and applications and be handy with open source data ingestion and processing frameworks. As a data engineer, you are expected to be skilled in C#, Java, Python, Ruby, Scala and SQL. You should also know how to use ETL tools and REST-oriented APIs for creating and managing data integration, and for providing data analysts and business users with simplified access to prepared data sets.
Formal education required: Master's degree in computer science, statistics or another quantitative field.
Average salary: $102,864 base Glassdoor (Dec. 2020)
Experience needed: Many employers expect that you to have five or more years of experience in a data engineering role, whether or not you carry that title. As with other tech jobs, the devil is in the details and the software tools needed will vary. However, it's a fair bet that you'll need to:
- Build and optimize data pipelines, architectures and data sets
- Have experience with data visualization techniques and tools such as Redash and Tableau
- Know how to use workflow management tools like Airflow and Luigi
- Be proficient with big data tools, such as Hadoop
- Know object-oriented/object function scripting languages such as Python and Scala
- Be familiar with cloud services, such as RC2 and Redshift
Business Intelligence Analyst
Typical job requirements: Business intelligence (BI) analysts are a step closer to the data source in the great food chain that is data science. As such, they are expected to help ensure the accuracy of the data collected and stored, analyze data for market trends and patterns, create narratives from the data and help non-data experts understand the insights from the data. Often, BI analysts are asked to build visualizations to help business users understand customer behavior. BI tools, once primarily used by BI and IT professionals, are now used more widely by business analysts, executives and workers, thanks to self-service BI and data discovery tools. Self-service business intelligence environments enable business users to query BI data, create data visualizations and design dashboards on their own.
Formal education required: At minimum, a bachelor's degree or impressive work experience.
Average salary: $76,402 base Glassdoor (Dec. 2020)
Experience needed: Employers often look for BI analysts who have experience working with business users. You need to be able to identify the relevant data and recommend a technical solution to make the best use of these data. Given all this, you'll likely be asked for your experience building and maintaining Tableau dashboards and Salesforce reports. You'll also want to have experience working with statistical packages, such as R, Python and MATLAB. It doesn't hurt to have a strong machine learning background, as well.
Machine Learning Engineer
Typical job requirements: Machine learning engineers conduct original research on large open source data sets, build models for understanding vast amounts of data, test the robustness and reliability of production models, and work closely with software engineers.
Formal education required: Some employers post this position asking only for a bachelor's degree. But the field is competitive, and most jobs require graduate degrees in machine learning, computer science, electrical engineering or a relevant field.
Average salary: $114,121 base Glassdoor (Dec. 2020)
Experience needed: Machine learning engineers are expected to be adept at implementing computer vision and machine learning algorithms and working with software engineers to bring the algorithms to life in commercial products. You're expected to have core programming expertise, such as Python, NumPy, SciPy, Pandas, MATLAB or R and to have experience with advanced machine learning methods. You are also expected to have experience modeling real data and have strong research skills.
Typical job requirements: Business analysts serve as the liaison between business and technology. Often, they are required to work on an Agile development team and be familiar with Agile best practices. You'll be expected to shepherd new business processes through technology solutions, working closely with data architects and developers to translate business requirements into technology designs and solutions.
Formal education required: Some employers look for candidates to have a master's or bachelor's degree in computer science, operations research, mathematics, computer science, cost accounting or a related scientific or technical discipline. However, because the parameters of this job description vary widely, employers also look for applicants with a business degree and strong business leadership skills, including experience in Agile environments.
Average salary: $68,346 base Glassdoor (Dec. 2020)
Experience needed: Those seeking a junior position as a business analyst are typically asked to have one to three years of relevant experience, including work in software development in some capacity. Those looking for a senior role might be expected to have at least five years as a business analyst, including active participation in Agile processes and data visualization.