Description

Architect and implement end-to-end reusable, scalable data pipelines that acquire, wrangle, integrate, and assure the quality of structured, unstructured, geospatial, LiDAR point-cloud, and real-time data from disparate sources to support product/service development and client program delivery.

Design, develop, validate and deploy analytical and predictive models, including geospatial analysis, time-series forecasting, inference, ensemble methods, and ML techniques to produce outputs that inform infrastructure planning, operational decision-making, and regulatory analysis.

Build and operationalize interactive visualizations, dashboards, and analytical interfaces that translate complex model outputs and statistical findings into clear, actionable insights for technical and non-technical audiences.

Lead the incubation of new data-centric products/services, collaborating with national service leads to elicit requirements, rapidly prototype concepts using lean/startup methods, define scalable architectures, tech stacks, and deployment protocols aligned with market needs.

Develop internal pipelines and transformation workflows that extract, integrate, and operationalize data from model-based AEC digital deliverables, including 3-, 4-, and 5-dimensional design, schedule, and cost models to support client-facing digital delivery programs.

Execute full-cycle technical work on client contracts. Perform exploratory analysis, feature engineering, statistical modeling, and deployment of data and analytic solutions that enable data-driven decision making within an operational context.

Establish and evolve firm-wide standards, frameworks, and best practices for applied data science, statistical methods, visualization principles, model lifecycle management, and responsible AI as the technical foundation for the DTS Center of Excellence.

Lead a community of practice, driving knowledge sharing, technical deep-dives, capability building, and the advancement of data and analytical standards across Client’s business units.

Performs all other duties as assigned.

Minimum Qualifications:

Education

Master’s degree in Data Science, Computer Science, Statistics, Applied Mathematics, Geography, or a related quantitative field with 5 years experience.

Bachelor’s degree and 10 years experience in Lieu of Masters.

Experience

Typically requires 10 years of hands-on experience in applied data science, analytics engineering, and systems modeling.

Minimum of 5 years in client-facing, consulting, or business development roles where analytic solutions were proposed, scoped, and delivered.

License/Certification

None required at time of hiring.

Knowledge, Skills, and Abilities (KSAs)

Mastery of the full Python data science ecosystem (pandas, Polars, NumPy, SciPy), advanced SQL, and R.

Expert proficiency in statistical modeling and machine learning frameworks (scikit-learn, statsmodels, PyTorch, TensorFlow) including time-series forecasting, causal inference, feature engineering, and advanced data sampling/resampling techniques for imbalanced or sparse infrastructure data.

Strong expertise in geospatial analytics and LiDAR processing using ArcGIS, PostGIS, GeoPandas, GDAL/OGR, PDAL, and related libraries for vector, raster, point-cloud, and sensor data.

Deep MLOps and model lifecycle experience: containerization, orchestration, CI/CD pipelines, experiment tracking, model monitoring, drift detection, and production deployment on Azure, AWS, or GCP.

Proficiency in developing and operationalizing advanced interactive visualizations and dashboards using Power BI, Tableau, Plotly/Dash, and geospatial visualization tools.

Advanced knowledge of responsible AI, data governance, bias detection/mitigation, model explainability, uncertainty quantification, and statistical precision and reliability in regulated environments.

Exceptional analytical problem-solving skills and the ability to clearly communicate complex methods, results, limitations, and recommendations to both technical teams and non-technical stakeholders.

** strong Power BI Experience**

Preferred Qualifications:

Domain experience in transportation, aviation, buildings, or construction operations (e.g., traffic sensors, tolling, BAS/BMS, BIM/VDC, facility telemetry).

Background in digital simulation environments, operational technology integration, or AI applied to infrastructure systems.

Familiarity with foundation model fine-tuning or multimodal modeling (imagery, geospatial, text, sensor data).

Prior experience with lean product incubation or innovation processes

Key Skills

Education

Bachelor's or Master's degrees