As you can imagine, data science is a pretty loose term or big tent idea overall. Though just about every industry imaginable utilizes the skills of a data-focused professional, each has its own challenges, needs, and desired outcomes. This is why you’ll often find that there are jobs in AI specific to an industry, or desired outcome when it comes to data. For example, a data scientist would be a good fit for a team that is in charge of handling large swaths of data and creating actionable insights from them. In another industry what matters is being able to predict behaviors in the medium and short terms, and this is where a machine learning engineer might come to play.
So let’s go ahead and look at some titles for jobs in AI, and industries that are similar to data scientists, but produce specific services for their niche. We’ll go from the most common titles and then move on to titles that might be new to you!
When people outside of data science think of those who work in data science, the title Data Analyst is what often comes up. What makes this job title unique is the “Swiss army knife” approach to data. Though seen in a variety of industries, including finance, eCommerce, marketing, healthcare, and government, a data analyst can be expected to perform analysis and interpretation of complex data to help organizations make informed decisions. This job title is the most likely to be in front of non-technical stakeholders explaining the results of the analysis in order to assist them to make the best-informed decisions.
Though scripted languages such as R and Python are at the top of the list of required skills for a data analyst, Excel is still one of the most important tools to be used. But this doesn’t mean they’re off the hook on other programs. Because they are the most likely to communicate data insights, they’ll also need to know SQL, and visualization tools such as Power BI and Tableau as well.
Though in many respects, quite similar to data analysts, you’ll find that business analysts most often work with a greater focus on industries such as finance, marketing, retail, and consulting. And unlike data analysts, their jobs will also entail the requirement of focusing on revenue models and referencing histories, and more to create complex reports, documents, and dashboards for management who need such data to make important business-related decisions. This includes projects related to business optimization, strategic planning, and system implementation.
Some of the tools and techniques unique to business analysts are pivot tables, financial modeling in Excel, Power BI Dashboards for forecasting, and Tableau for similar purposes.
Machine Learning Engineer
Machine learning engineers will use data much differently than business analysts or data analysts. For them, models are key and they spend their time constructing complex machine learning models and systems that can analyze and make predictions based on data on behalf of their organization. Because of the powerful benefits of having predictive models, you can find that machine learning engineers work in a diverse range of industries such as finance, healthcare, transportation, and e-commerce.
Unlike data and business analysts, machine learning engineers will use Python and Python-based frameworks such as TensorFlow and PyTourch to develop and train their models. As models become more complex and the needs of the organization evolve and demand greater predictive abilities, you’ll also find that machine learning engineers use specialized tools such as Hadoop and Apache Spark for large-scale data processing and distributed computing. Tools such as the mentioned are critical for anyone interested in becoming a machine learning engineer.
Data engineers are the authors of the infrastructure that stores, processes, and manages the large volumes of data an organization has. The main aspect of their profession is the building and maintenance of data pipelines, which allow for data to move between sources. For the data analyst, business analyst, or even machine learning engineer, the data engineer can be considered the “gatekeeper” of the data they require in order to do their job. They provide the environment in which data can be stored, extracted, and even analyzed when ended.
Because of the explosion of data over the last few years, you can expect to see data engineers working in industries such as finance, healthcare, the public sector, e-commerce, and media. Like their counterparts in the machine learning world, engineers need to know a variety of scripted languages such as SQL for database management, Scala, Java, and of course Python. These programs allow them to design and build scalable and efficient data pimples that can handle large volumes of data, and ensure that the data is stored in a secure and reliable manner.
Data Visualization Specialist
As organizations grow, job titles begin to become more specialized and you’ll begin to see titles such as data visualization specialist. Due to the high demand for actionable insights, more medium to large organizations see the benefit of having a data professional who specifically manages the creation of visual representations of data that will be used to communicate critical insights to stakeholders. These specialists will often work closely with both data analysts and data scientists to understand the data and the insights that need to be communicated. Because of this, what is produced is often complex data insights that deal with large swaths of information.
As you can imagine, you’ll find these data professionals are partially in demand in industries such as healthcare, finance, and marketing where dashboards and other visuals help make critical decisions. Another part of their job is to play that key role between the data team and stakeholders and act as a bridge between the two, so that there isn’t a blocker between the two. Some of the tools you can expect to see used will be Power BI and Tableau
Before you ask, yes a data architect and a data engineer are quite different. Think back to the architect in the popular movie franchise, The Matrix, data architects are in charge of designing and building out the entire data architecture an organization will depend on. Because of this, they will be required to work closely with business stakeholders, data teams, and even other tech-focused members of an organization to sure that the needs of the organization are met and comply with overall business objectives.
As you can imagine, data architects require a strong background in database design, data modeling, and data management. Because of this, the tools they depend on will be a bit different than what we’ve talked about so far. Technologies such as SQL Server, Oracle, and MySQL, for database design and modeling tools like ERwin and Visio. Like data engineers, with the explosion of data over the last few years, the demand for data architects is quite diverse. You’ll see them in everything from healthcare, finance, e-commerce, NGOs, and marketing.
Operations Research Analyst
With the growth of data so does its complex nature when it comes to management, analytics, and operations optimizations. This is where an operations research analyst comes to play. This is a position that requires a mathematical and analytical methodology to assist organizations to solve complex problems and make data-driven decisions in dynamic environments. Due to the nature of the job, these analysts require a strong background in mathematics, computer science, and statistics to get the job done.
To bring it all together, they also must be proficient in languages such as Python, R, and MATLAB for data, and Gurobi and CPLEX for mathematical modeling so they can design and implement optimization algorithms, simulation models, and decision support tools. Because of this, you’ll see that industries such as supply chain management, transportation, logistics, maritime logistics, and other similar industries have a high demand for operation research analysts.
You’re likely noticing a trend. As data grows in complexity, partially in finance, health care, and even the social sciences with NGOs, the need for data professionals who can collect, analyze and interpret data through statistical methods grows. These professionals are responsible for designing experiences, analyzing data, and drawing insights from their work. They’ll in turn take these findings to stakeholders, and present them in a clear and concise manner so thoughtful conclusions can be made.
Because of this, statisticians need to have a strong background in both mathematics and statistics while also having advanced proficiency in statistical software and scripting languages such as R, SAS, and SPSS. These allow them to collect data, design experiments, design statistical models to analyze data to draw conclusions, and then interpret findings through patterns found.
Like statisticians, research scientists are professionals who conduct research and development in a variety of fields that use data for findings. Though they may not focus as heavily on statistical modeling as in the previous entry, they are still responsible for designing experiments, analyzing results, and presenting findings to stakeholders. Though they use data, they may not be as well versed in languages such as R or Python. This is because this job title is one of the most loosely defined as each organization that employs research scientists will have different needs and those needs will shape the skills required.
But what you’ll still find is the need to use machine learning, statistical software, techniques, and other methods to conduct research. Because of this, you’ll find research scientists are in very high demand in the pharmaceutical and biotech industries. There they play a critical role in advancing knowledge and solving complex problems and helping organizations stay competitive in their respective markets.
Data Governance Manager
Believe it or not, data requires rules to stay consistent, accurate, and secure. If not, any findings or insights found by other teams in the data science world could be compromised and that is where a data governance manager comes into play. These professionals are responsible for ensuring that an organization’s data is compliant with regularity requirements. As you can imagine, industries such as healthcare, finance, and government agencies need these managers as they are responsible fr for developing and implementing policies and procedures to manage data quality, privacy, and security.
Like their counterparts, data engineers and data architects, data governance managers need a strong background in data management but differ as they also need to have a strong background in regularity requirements and best practices for data management. As governments around the world pass laws protecting the data of individual citizens, organizations that store data need professionals whose jobs are to ensure the law is followed, data is accurate and data is secure so that other data professionals who need it to communicate to stakeholders have the best possible data available.
How to get ready for any of these jobs in AI and industries
Are you ready to tackle any of these job paths and or industries? Well then, you’re in luck. ODSC East 2023 this May and ODSC Europe this June have you covered. From practical training, hands-on workshops, networking events, and more. Both events can help you fill in the gaps you have that can hinder your future success as a data scientist. So, what are you waiting for? Register today and build the tomorrow you want. Act now, as ODSC East is 40% off for a limited time, while ODSC Europe is 60% off for a limited time.
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