What Do Data Scientists Do? A Guide to AI Maturity, Challenges, and Solutions

The future of business depends on artificial intelligence and machine learning.

According to IDC, 83% of CEOs want their organizations to be more data-driven. 87% of CXOs shared that becoming an intelligent enterprise was their top priority.

Data scientists could be your key to unlocking the potential of the Information Revolution—but what do data scientists do? How can they help you determine strategy and attain your business goals?

What Do Data Scientists Do?

Data scientists drive business outcomes. Many implement machine learning and artificial intelligence to tackle challenges in the age of Big Data. They develop and continuously optimize AI/ML models, collaborating with stakeholders across the enterprise to inform decisions that drive strategic business value.

What data scientists do is directly tied to an organization’s AI maturity level.

Awareness and Activation

When businesses enter the AI arena for the first time, they’re often tempted to look for accelerated results and immediate growth. However, caution and careful planning are essential in this stage. Decision makers need to trust application leaders to guide the incremental steps that AI initiatives require. 

Once an organization has identified its AI use cases, data scientists informally explore methodologies and solutions relevant to the business’s needs in the hunt for proofs of concept. These might include—but are not limited to—deep learning, image recognition and natural language processing. Sometimes, even a simple linear regression might do the trick. 

At this level, the data science team will be small or nonexistent. But potential use cases could increase after AI delivers promising results and organizational confidence grows.

Businesses will then require more information-literate staff, but they’ll need to contend with an ongoing shortage of data scientists. As a result, they’ll require upskilling initiatives or additional data scientists.

If you’re just getting started with AI and ML, technology can help you bridge gaps in your workforce and institutional knowledge. DataRobot AI Platform supports business analysts and data scientists by simplifying data prep, automating model creation, and easing ML operations (MLOps). These features reduce the need for a large workforce of data professionals.

At the same time, automated ML tools can augment your existing data professionals’ expertise without sacrificing their time. Automation also makes AI-driven forecast models possible at scale, which further minimizes your costs by accurately forecasting demand.

Operationalization

At the operational level, organizations have deployed several AI models serving different business needs into production. As a result, initiatives have buy-in from executives and a dedicated budget. Increased scale and integration into a wide array of business processes means that data scientists need to tackle growing AI and ML project backlogs.

At this level, where business requests for models start trickling in, data scientists focus on accelerating ML model building and use-case prioritization. They work cross-functionally, from data ingestion to model deployment.

Challenges at this stage are associated with the organization’s growing AI and ML footprint. Collaboration often hinders efficiency as teams and projects scale. As a result, organizations need a standardized platform that enables seamless collaboration between data scientists, business analysts, IT, and other groups across the enterprise.

If your business operates at this level, it’s likely that you still need to optimize your limited workforce. An enterprise cloud platform featuring a unified environment built for continuous optimization can help you accelerate building, testing, and experimenting with AI models and reduce demands on your data professionals.

If your business is at this stage, the automation available through enterprise AI platforms can optimize your time and budget even further. Features like DataRobot Automated Machine Learning and Automated Time Series reduce backlogs by augmenting your data scientists’ expertise and rapidly applying advanced forecasting models.

Finally, tools that streamline delivery and enable accurate forecasting through automation will power growth and help you anticipate demand. It will go a long way to significantly amplify the productivity of your data scientists.

Systemization

Organizations at this level have reached an advanced stage of AI maturity. With a robust ML infrastructure in place, these enterprises consider implementing AI for all digital projects. Groups across the enterprise, including process and application design, understand the value of data. So, AI-powered applications can provide benefits throughout the business ecosystem.

Companies at this stage will likely have a team of ML engineers dedicated to creating data pipelines, versioning data, and maintaining operations monitoring data, models & deployments.

By now, data scientists have witnessed success optimizing internal operations and external offerings through AI. They work to re-train and optimize AI models as they mitigate model bias to ensure fairness and align with corporate ethics. As the internal footprint of AI increases, teams need to secure proper model governance to mitigate risk in compliance with regulations.

Organizations at this level still face many challenges. Meanwhile, maintaining intellectual property (IP) due to workforce churn can break processes and necessitate costly and time-consuming reverse engineering.

Finally, data scientists ensure proper AI governance, ethics, and risk management to avoid unintended or unforeseen effects. The more organizations rely on AI and ML, the more risk they’ll experience related to regulatory compliance.

Time and budget are also crucial considerations. A centralized platform like DataRobot MLOps provides a single solution for deploying, monitoring, managing, and governing all production models. It can significantly reduce both the time and the investment that operationalizing your ML requires.

Technology also mitigates the issues that arise from scale and churn. Streamlining deployment with a unified MLOps platform saves you time and money at scale, maintaining peak performance—even as your AI initiatives grow. A full-featured enterprise platform also helps sustain your IP by establishing robust governance protocols, regardless of your staff turnover rates.

Data Scientists: The Engine of an AI-driven Enterprise

Depending on your organization’s AI maturity, data scientists can cover a wide range of responsibilities and functions. Their relevance to your business depends on the stage of your AI journey.

The rapid proliferation of AI and ML in the face of a data science talent shortage means that automation is becoming increasingly important. When hiring more team members is tricky, automation is your only option for growth.

Regardless of the maturity of your AI program, choosing a holistic platform will help your data scientists accelerate deployment and optimize their models to meet business needs and drive results.