Milena Afeworki: Bridging the Gap Between Civil Engineering and Data Science

The field of civil engineering is on the cusp of a data revolution. With the proliferation of sensors, drones, and satellites collecting vast amounts of information about our built environment, civil engineers are gaining unprecedented insights into the performance and sustainability of the infrastructure systems we depend on every day. At the forefront of this transformation are pioneers like Milena Afeworki, who are leveraging cutting-edge data science techniques to solve complex engineering challenges and drive innovation in the construction industry.

Milena began her career as a civil engineer in her home country of Eritrea, working on infrastructure design projects for public and NGO clients. With a strong background in mathematics and a knack for data visualization, she quickly realized the potential for data-driven decision making to optimize engineering processes and outcomes. "I was passionate about solving problems using mathematical analysis, design, and visualization," Milena recalls. "But as I advanced in my career, I saw how much data was behind the projects being launched, and I wanted to be part of the bigger determining factor that drives those decisions."

The Age of Big Data in Civil Engineering

Milena‘s realization mirrors a growing trend in the civil engineering industry. According to a 2023 report by McKinsey Global Institute, the construction sector has lagged behind others in digital transformation, but is now poised for disruption as firms increasingly adopt AI-powered analytics, 5D building information modeling (BIM), and other data-centric technologies. The report projects that these innovations could boost the sector‘s productivity by up to 15 percent and generate $1.6 trillion in additional value globally by 2035.

One area where data science is already making a significant impact is in the realm of predictive maintenance for critical infrastructure assets. By analyzing vast troves of sensor data on things like bridge vibrations, wind loads, and traffic patterns, machine learning algorithms can identify early warning signs of structural damage or degradation, enabling engineers to proactively prioritize repairs and prevent catastrophic failures.

For example, researchers at Cambridge University recently developed a deep learning model that can detect and localize cracks in concrete bridge decks with over 90% accuracy, based solely on images captured by a drone. By automating the inspection process and identifying problems early, this technology could help extend the service life of bridges and improve public safety, all while reducing costs and resource requirements.

Data science is also transforming the way civil engineers approach construction project management and delivery. Platforms like Autodesk‘s BIM 360 enable real-time collaboration and data sharing among project stakeholders, from the initial design phase through to operations and maintenance. By integrating data from multiple sources, including 3D models, schedules, budgets, and field reports, these tools provide a single source of truth that helps teams make informed decisions, reduce rework and delays, and ultimately deliver projects faster and more efficiently.

Bridging the Skills Gap

Despite the clear benefits of data-driven engineering, many civil engineers lack the specialized skills and knowledge needed to fully leverage these emerging technologies. That‘s where trailblazers like Milena Afeworki come in. After several years working as a civil engineer in Eritrea, Milena made the bold decision to transition into data science and move to the United States, where she saw greater opportunities to apply her skills in a cutting-edge field.

To gain the necessary expertise in programming, statistics, and machine learning, Milena enrolled in an intensive data science bootcamp at the Programming School. "Programming School was my first choice because of its reputable data science program and structured career coaching," she explains. "I wanted to gain hands-on skills and practical experience in designing and building data science projects applying machine learning tools."

Over the course of the 15-week program, Milena immersed herself in Python, SQL, data visualization, and a host of other essential data science skills. While the accelerated pace of learning was challenging coming from a different background, she found the real-world project work to be especially valuable in preparing her for a career transition. "These projects simulated the challenges that data scientists face in the industry, allowing me to apply the concepts I learned, incorporating my previous experience and enhancing my problem-solving skills," Milena notes.

Another key aspect of the bootcamp experience for Milena was the personalized career coaching, which helped her navigate the job search process and land her first role as a data integration engineer after graduation. "My career coach played a crucial role in providing guidance on crafting my resume, preparing for interviews, and expanding my network," she says. "Her professional insights and emotional support helped me navigate the competitive job market and ultimately secure my first data engineering role."

Putting Data to Work in the Field

In her current position as a data analyst, Milena is putting her skills to work on projects that blend her civil engineering background with cutting-edge data science techniques. One recent example involved designing a machine learning model to predict the structural condition of bridges across the United States, based on climate data from NASA and bridge inspection records from the Department of Transportation.

"The purpose of the project was to enable the bridge management system to accurately predict future bridge conditions and help make informed decisions on when and where to allocate maintenance budgets," Milena explains. "By leveraging historical data on things like temperature, precipitation, and traffic loads, we were able to train a classification model that could identify bridges at risk of deterioration and recommend optimal repair schedules."

This kind of predictive analytics is just one example of how data science is revolutionizing the way civil engineers approach their work. From optimizing construction site layouts using generative design algorithms, to analyzing commuter patterns to inform transportation planning decisions, the possibilities are virtually endless. And with the amount of data being generated by our built environment growing exponentially each year, the demand for professionals with both domain expertise in civil engineering and technical skills in data science is only going to increase.

Charting a Path Forward

For Milena, the journey from civil engineer to data scientist has been challenging but immensely rewarding. Looking back on her experience at the Programming School and her early years in the industry, she credits her success to a combination of adaptability, persistence, and a growth mindset. "The most significant lesson I gained from my time at Programming School was developing the skill to thrive in uncertain and ambiguous scenarios," she reflects. "I‘ve come to view these situations as valuable opportunities for me to inquire, observe, learn, and embrace mistakes, all of which contributed to my growth beyond my initial capabilities."

As for advice to other civil engineers considering a similar career transition, Milena encourages them to start exploring data science concepts and tools on their own, whether through online tutorials, MOOCs, or side projects. She also stresses the importance of building a strong professional network and seeking out mentors who can provide guidance and support along the way.

"There‘s so much exciting work happening at the intersection of civil engineering and data science right now," Milena says. "Whether you‘re interested in sustainable infrastructure, smart cities, or disaster resilience, there are countless opportunities to make a real impact by leveraging data in creative ways. The key is to stay curious, keep learning, and don‘t be afraid to take risks and try new things."

Looking ahead, it‘s clear that the future of civil engineering will be increasingly driven by data and advanced analytics. As more firms adopt these technologies and the talent pipeline continues to grow, we can expect to see a wave of innovation that will transform the way we design, build, and maintain the infrastructure that underpins our society. And with pioneers like Milena Afeworki leading the charge, that future looks brighter than ever.