why high-salaried data scientists are not always the best option for ai/ml development and who to hire instead

Artificial intelligence (AI) and machine learning (ML) are hot topics in nearly every industry. More than 92% of businesses report measurable results from AI investments, driving the worldwide market for AI at a 28.5% compound annual growth rate to a predicted $827 billion by 2030. This massive growth is leading to an increased demand for data scientists. For example, the demand for generative AI talent alone increased 30X between January 2023 and February 2024. It’s no wonder that 33% of organizations say that limited AI skills and expertise are a top barrier to successful AI adoption.

But industry innovations mean that not every AI/ML model requires a team of skilled data scientists. Knowing which talent to tap for each phase of the AI/ML development lifecycle — and which use cases may not require a data scientist at all — can help you optimize resources so you can continue innovating at scale with this exciting new technology.

data scientists aren’t required for the most time-consuming data tasks.

Data scientists skilled at AI/ML model development are scarce. Once they accept a position, many are frustrated by the need to spend 80% of their time on data wrangling, performing tasks such as finding, connecting, and cleaning data, rather than on high-value activities like model development and optimization. Because data scientists are in high demand, command high salaries, and prefer to spend their time doing work they’re trained for, this waste of resources is triply problematic.

While highly skilled PhD data scientists undoubtedly bring valuable skills to the table, you don't necessarily need them for successful AI/ML development in every case. You can often achieve the same outcomes by leveraging data analysts for data wrangling. These professionals, while commanding lower salaries, have a strong background in statistics and data analysis and can effectively extract, transform, and load (ETL) data from various sources into a centralized data pipeline to feed AI/ML models.

low-code/no-code development is flattening the skills curve for ai/ml use cases.

Once the data is aggregated, the evolution of low-code/no-code development platforms further transforms the AI/ML development process. Low-code/no-code development platforms provide user-friendly interfaces and drag-and-drop features that let users with limited coding expertise create, train, and deploy AI/ML models. This allows you to reduce your reliance on high-salaried PhD data scientists and empower line of business users to develop AI/ML applications — no data scientists required.

scenarios where data analysts plus citizen developers make sense.

There are several scenarios where data analysts and line of business users can successfully create AI/ML applications using low-code/no-code platforms. These include:

Business intelligence and analytics: Creating predictive models for sales forecasting, customer churn prediction, or marketing campaign optimization that can be used to make data-driven decisions and improve overall business performance
Process automation: Automating repetitive tasks such as data entry or customer support tasks to streamline daily workflows and reduce manual labor
Marketing: Enabling personalized marketing campaigns and product recommendations to improve customer engagement and drive sales
Predictive maintenance: Creating models that predict equipment failures or maintenance needs in manufacturing or logistics industries to minimize downtime
Fraud detection: Developing AI/ML models that detect fraudulent transactions or activities in real-time to prevent financial losses and strengthen security measures
Chatbots and virtual assistants: Designing conversational AI applications that address common customer queries to improve customer satisfaction or assist with internal processes to enhance efficiency

In these scenarios, data analysts' skills in data wrangling, statistics, and basic ML techniques are sufficient to create data pipelines for the low-code/co-code platforms business users can leverage to create AI/ML applications that deliver significant value to the organization.

scenarios where data scientists add value.

While data analysts and line of business employees can handle many aspects of AI/ML development using no-code/low-code platforms, there are certain use cases and industry verticals where the expertise of a data scientist is invaluable. For example:

Research and development: In industries or organizations focused on pioneering new technologies and methodologies, data scientists with PhD-level expertise can bring depth and breadth of knowledge needed for innovative research and development.
Complex predictive models: Industries such as finance and insurance often require intricate predictive models and advanced statistical analysis. Data scientists with a strong background in mathematics and statistical learning can provide valuable insights that may be beyond the scope of data analysts.
Natural language processing (NLP) and computer vision: In industries like marketing, healthcare, and autonomous vehicles where advanced NLP or computer vision tasks are used for sentiment analysis or object recognition, a data scientist's in-depth understanding of ML algorithms can be crucial for achieving accurate results.
Large-scale data processing: Industries dealing with large-scale data, such as telecommunications and e-commerce platforms, often require data scientists to design efficient algorithms and distributed computing solutions to process and analyze data effectively.

However, even in these cases data analysts can be used for the first phase of data wrangling to free data scientists’ time for innovation.

the need for many different types of talent.

No matter which path you choose, you will need to rely on expert talent to help you adapt to the growing demand for AI/ML applications without compromising on quality or speed of delivery. Partnering with Randstad Digital gives you the strength and flexibility to access specialized talent with diverse skill sets and experience levels, and quickly scale up or down based on business requirements. This can reduce the costs associated with hiring, training, and retaining permanent employees and help ensure that any on staff data scientists are working on the most critical and fulfilling projects.

the future of ai/ml development.

As low-code/no-code development platforms continue to mature, we can expect a more significant shift toward leveraging data analysts for the initial stages of AI/ML development. By employing data analysts to manage data pipelines, companies can lower costs and improve efficiency while still achieving desired outcomes. Businesses that embrace this approach will be better positioned to capitalize on the AI/ML revolution.

Learn more.

By ensuring your data is primed and ready, you can fully embrace the promise and capabilities of AI solutions. See how Randstad Digital can help you unlock the power and value hidden within your data.

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