Data Scientist
New York, NY
True Environmental – True Environmental /
Hybrid
True Environmental and its partner firms, have united their strengths to establish the nation's premier environmental and engineering services consulting firm. This powerhouse organization offers an extensive range of services aimed at revolutionizing the built environment and preserving the natural one. Its diverse expertise encompasses environmental remediation, urban planning, engineering iconic coastal and marine structures, developing sustainable energy sources, and pioneering resource extraction methods. With a workforce of over 450 dedicated professionals, including environmental experts, engineers, technicians, scientists, planners, surveyors, and construction management professionals, this dynamic organization operates in more than 20 offices across the U.S. Explore further at True-Environmental.com to discover the full scope of our capabilities.
We are seeking a highly analytical and innovative Data Scientist to support our engineering teams with advanced data-driven insights. This role will focus on leveraging data science, machine learning, and statistical modeling to optimize engineering processes, improve product design, enhance reliability, and drive operational efficiency. The ideal candidate will combine technical expertise with a strong understanding of engineering systems, manufacturing processes, and applied analytics.
What you'll do
- Analyze engineering and manufacturing data to identify patterns and actionable insights.
- Develop predictive models to support quality assurance, process optimization, predictive maintenance, and product performance analysis.
- Partner with engineers, designers, and R&D teams to define data requirements and provide evidence-based recommendations.
- Build and maintain scalable data pipelines and machine learning models for real-time applications e.g., anomaly detection, fault prediction).
- Create digital twins, simulation models, or optimization algorithms to support engineering decision-making.
- Visualize complex engineering data and present findings to stakeholders in clear, actionable formats.
- Ensure compliance with industry standards, data governance, and cybersecurity best practices.
- Stay current with emerging data science methods and their applications in engineering (e.g., Industry 4.0, AI in design, advanced materials).
· Design and implement modern, scalable data architectures to support AI/ML workloads.
· Implement solutions integrating structured, semi-structured, and unstructured data.
Required Qualifictions
- Bachelor’s or Master’s degree in Data Science, Engineering, Computer Science, Statistics, or related field.
- Strong programming skills in Python, R, SQL; experience with MATLAB or C++ is a plus.
- Hands-on experience with machine learning frameworks (e.g., Scikit-learn, TensorFlow, PyTorch).
- Familiarity with engineering software and data (e.g., CAD/CAE, IoT/SCADA systems, sensor data streams).
- Knowledge of predictive modeling, optimization algorithms, and statistical process control.
- Proficiency with Power BI
- Solid understanding of probability, statistics, and experimental design.
Preferred Qualifications
- Advanced degree (Master’s or Ph.D.) in Data Science, Engineering, or Applied Mathematics.
- Experience applying data science to predictive maintenance, supply chain optimization, or manufacturing automation.
- Familiarity with big data platforms (e.g., Spark, Hadoop) and cloud environments (AWS, Azure, GCP).
- Knowledge of digital twin technology, IoT analytics, or reliability engineering.
- Prior experience in mechanical, electrical, civil, or manufacturing engineering industries.
- Strong problem-solving skills with an engineering mindset.
- Ability to communicate complex data insights to engineers, operations staff, and leadership.
- Collaborative, with experience working on cross-disciplinary teams.
- Curiosity-driven and detail-oriented with a focus on continuous improvement.
All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability or protected veteran status.
