Sr. Data Scientist

DPR Construction•Dallas, TX

30d

About The Position

DPR Construction is seeking a skilled Senior Data Scientist to help advance our data-driven approach to building. In this role, you'll use statistical analysis, machine learning, and data visualization to turn complex construction and business data into actionable insights that improve project planning, cost forecasting, resource management, and safety. Working with project and operations teams, you'll build and deploy scalable, secure data solutions on cloud platforms like Azure and AWS, driving innovation and operational excellence across DPR's projects.

Requirements

Bachelor's or Master's degree in Data Science, Computer Science, Statistics, Engineering, or a related field.
7+ years of experience in data science roles within AEC, product or technology organizations.
At least 4 years of experience working with cloud platforms, specifically Azure and AWS, for model deployment and data management.
Strong proficiency in Python or R for data analysis, modeling, and machine learning, with experience in relevant libraries (e.g., Scikit-learn, TensorFlow, PyTorch) and NLP frameworks (e.g., GPT, Hugging Face Transformers).
Expertise in SQL for data querying and manipulation, and experience with data visualization tools (e.g., Power BI, Tableau).
Solid understanding of statistical methods, predictive modeling, and optimization techniques.
Expertise in statistics and causal inference, applied in both experimentation and observational causal inference studies.
Proven experience designing and interpreting experiments and making statistically sound recommendations.
Strategic and impact-driven mindset, capable of translating complex business problems into actionable frameworks.
Ability to build relationships with diverse stakeholders and cultivate strong partnerships.
Strong communication skills, including the ability to bridge technical and non-technical stakeholders and collaborate across various functions to ensure business impact.
Ability to operate effectively in a fast-moving, ambiguous environment with limited structure.
Experience working with construction-related data or similar industries (e.g., engineering, manufacturing) is a plus.

Nice To Haves

Familiarity with construction management software (e.g., ACC, Procore, BIM tools) and knowledge of project management methodologies.
Hands-on experience with Generative AI tools and libraries.
Background in experimentation infrastructure or human-AI interaction systems.
Knowledge of time-series analysis, anomaly detection, and risk modeling specific to construction environments.

Responsibilities

Data analysis and modeling: Analyze large datasets to identify trends, bottlenecks, and areas for improvement in operational performance. Build predictive and statistical models to forecast demand, capacity, and potential issues.
Develop and deploy models: Build, test, and deploy machine learning and AI models to improve operational processes.
Analyze operational data: Examine data related to projects, production, supply chains, inventory, and quality control to identify patterns, trends, and inefficiencies.
Optimize processes: Use data-driven insights to streamline workflows, allocate resources more effectively, and improve overall performance.
Forecast and predict: Create predictive models to forecast outcomes, such as demand, and inform strategic decisions.
Communicate findings: Present findings and recommendations to stakeholders through reports, visualizations, and presentations.
Ensure reliability: Build and maintain reliable, scalable, and efficient data science systems and processes.
Collaboration: Partner with project managers, engineers, and business leaders to ensure data solutions are aligned with organizational goals and deliver tangible improvements.
Continuous Learning: Stay current with advancements in data science and machine learning to continually enhance the company's data capabilities.
Reporting and communication: Create dashboards and reports that clearly communicate performance trends and key insights to leadership and other stakeholders. Translate complex data into actionable recommendations.
Performance monitoring: Implement data quality checks and monitor the performance of models and automated systems, creating feedback loops for continuous improvement.
Experimentation: Design and evaluate experiments to quantify the impact of new systems and changes on operational outcomes.