ML Engineer and Data Scientist Roles: Solving Business Challenges Every Day

As an ML Engineer or Data Scientist, Brent Follin highlights the diverse and dynamic nature of work, emphasizing the role in addressing critical business problems through predictive models and machine learning algorithms. This article delves into the daily routine and specific tasks, providing insights into the ever-evolving responsibilities of these professionals.

Description of Business Problems Addressed

ML Engineers and Data Scientists, like Brent Follin, tackle a variety of complex business challenges. One of the primary tasks involves building predictive models to identify potential risks within certain industries. For instance, Brent's team develops models to predict the likelihood of truck drivers becoming involved in accidents or potentially quitting their jobs. These incidents can lead to significant financial costs, making accurate predictions vital for risk management.

Custom Models and Constant Monitoring

Each client receives custom-built models tailored to their unique business needs. This is due to the diverse nature of operations, including differences in geography, cargo types, and travel distances. Brent and his team are diligent in monitoring these models for performance, and when performance drops, they rebuild these models. This meticulous process ensures that the models remain effective and relevant over time. On average, a model is refreshed every six months for each client.

Post-Deployment Analytics and Reporting

Post-deployment, Brent and his team generate detailed reports and analytics aimed at proving the return on investment (ROI) for using their predictive models. They move beyond simple statements and instead provide segmental insights into why certain drivers might quit. This approach provides actionable data that can be utilized by business decision-makers to optimize their operations.

Diverse Projects and Responsibilities

ML Engineers and Data Scientists, like Brent Follin, engage in a wide range of projects beyond the predictive models and analytics. These activities include:

Customer Sentiment Analysis: Monitoring and assessing customer sentiment through survey text to gather feedback and improve product support. Predictive Vehicle Maintenance: Developing algorithms that predict when vehicles are likely to need maintenance, reducing unexpected downtime and improving fleet efficiency. Predictive Hiring for Truck Driving Jobs: Identifying ideal candidates for truck driving positions based on predictive analytics, ensuring a smoother onboarding process and better job fit. Ad-Hoc Analytics: Providing advanced analytics on an as-needed basis, helping clients make informed decisions with real-time data insights. Proof of Concepts: Envisioning and implementing cutting-edge tools through research and proof of concept projects, pushing the boundaries of what's possible in the industry.

These projects are just a few examples of the broad scope of duties and the relentless pursuit of innovative solutions that define the daily life of an ML Engineer or Data Scientist.

Conclusion

The role of an ML Engineer or Data Scientist is multifaceted, addressing a wide array of business challenges and driving operational improvements through predictive modeling and analytics. As businesses continually seek to optimize their processes and predict future trends, roles like those of Brent Follin become increasingly crucial in shaping the future of industries.

If you are interested in learning more about how ML Engineers and Data Scientists address business problems, take a look at the video from the meetup mentioned by Brent Follin.