Data Solutions Architect

RS&H • ,

2d

About The Position

RS&H is seeking an experienced Data Solutions Architect to design and deliver secure, scalable, and client-focused data and artificial intelligence solutions across our aviation, federal, transportation, aerospace, health and science, and corporate market sectors. In this highly influential technical role, you will transform complex and inconsistent data into trusted pipelines, analytical models, predictive solutions, and visualizations that support critical client decisions. You will also identify recurring market needs and help turn early-stage concepts into configurable, production-ready digital offerings. The majority of this role focuses on advancing data and AI solutions from concept through requirements, architecture, development, testing, validation, and deployment. You will establish technical standards, strengthen data governance and model lifecycle practices, and lead a community of practice that builds data capabilities across the organization. This position has no direct reports but provides authoritative technical direction, guidance, and mentorship to project teams, architects, engineers, and junior staff.

Requirements

• Bachelor’s degree in Data Science, Computer Science, Statistics, Applied Mathematics, Geography, or a related quantitative or technical discipline.
• Fifteen (15) or more years of experience in applied data science, analytics engineering, data architecture, or a related discipline.
• Full lifecycle experience developing data products, including concept development, requirements, architecture, model development, validation, deployment, and production support.
• At least five (5) years of experience in client-facing consulting or professional services, including proposing, scoping, and delivering analytical solutions.
• Current certification in a relevant technology domain or equivalent demonstrated expertise.
• Mastery of the Python data science ecosystem, including pandas, Polars, NumPy, and SciPy.
• Advanced proficiency in SQL and R.
• Expert knowledge of statistical modeling and machine learning frameworks, including scikit-learn, statsmodels, PyTorch, and TensorFlow.
• Demonstrated expertise in time-series forecasting, causal inference, feature engineering, and sampling or resampling techniques for imbalanced or sparse data.
• Strong experience with geospatial analytics and LiDAR processing using tools such as ArcGIS, PostGIS, GeoPandas, GDAL/OGR, and PDAL.
• Experience working with vector, raster, point-cloud, sensor, and operational data.
• Deep experience with MLOps and model lifecycle management, including containerization, orchestration, continuous integration and delivery (CI/CD), experiment tracking, model monitoring, drift detection, and production deployment.
• Experience deploying cloud-based solutions using Microsoft Azure, Amazon Web Services (AWS), or Google Cloud Platform (GCP).
• Advanced proficiency in interactive data visualization and dashboard development using Power BI, Tableau, Plotly/Dash, or geospatial visualization tools.
• Advanced knowledge of responsible AI, data governance, bias detection and mitigation, model explainability, uncertainty quantification, and statistical reliability in regulated environments.
• Exceptional analytical and problem-solving skills.
• Ability to communicate complex technical methods, results, limitations, and recommendations clearly to technical and non-technical stakeholders.
• Candidates must be legally authorized to work in the United States permanently. This position is not eligible for visa sponsorship or visa transfer.

Nice To Haves

• PhD in Data Science, Computer Science, Statistics, Applied Mathematics, Geography, or a related field.
• Experience productizing prototypes, including software-as-a-service (SaaS) delivery and lean product incubation processes.
• Domain experience in transportation, aviation, buildings, or construction operations.
• Experience with traffic sensors, tolling systems, building automation systems, building information modeling (BIM), virtual design and construction (VDC), or facility telemetry.
• Background in digital simulation environments, operational technology integration, or AI applications for infrastructure systems.
• Familiarity with foundation model fine-tuning or multimodal modeling involving imagery, geospatial data, text, and sensor data.

Responsibilities

• Lead the development of data, analytics, artificial intelligence, and visualization offerings, supporting solution scoping, client pursuits, production-grade delivery, governance, and data-practice enablement.
• Translate challenges in buildings, infrastructure, and construction management into reusable data and AI products with clearly defined interfaces and service levels.
• Design reusable solutions using application programming interfaces (APIs), event streams, curated datasets, feature stores, and visualization layers.
• Prepare system diagrams, architecture decision records, data contracts, reference implementations, and build-versus-buy recommendations.
• Architect geospatial intelligence solutions that integrate geographic information system (GIS) data, two-dimensional drawings, three-dimensional models, operational information, and sensor data.
• Develop and deploy analytical and predictive models using time-series forecasting, causal inference, ensemble methods, and other machine learning techniques.
• Create and operationalize interactive dashboards, analytical interfaces, and visualizations using Power BI and related tools.
• Translate model outputs and statistical findings into actionable insights for both technical and non-technical audiences.
• Advance associate-developed generative AI prototypes into secure, reliable, production-ready solutions.
• Establish data quality requirements, validation processes, and remediation approaches as solutions move from development into production.
• Incorporate data ownership, stewardship, lineage, quality, access controls, and model lifecycle management throughout solution development.
• Design solutions that address architecture, engineering, construction, and public-sector requirements, including Controlled Unclassified Information (CUI), NIST 800-171, Cybersecurity Maturity Model Certification (CMMC), client data isolation, operational technology and information technology separation, and Microsoft Government Community Cloud High (GCC High) environments.
• Develop reusable accelerators, including templates, schemas, connectors, evaluation frameworks, automated validation processes, authorization workflows, and infrastructure provisioning components.
• Evaluate new ideas against defined advancement criteria and help teams shape concepts into viable data and AI offerings.
• Collaborate with legal, finance, business subject matter experts, developers, and multidisciplinary product teams throughout the solution lifecycle.
• Lead and participate in client and stakeholder discovery sessions.
• Translate business needs and analytical questions into complete digital solution definitions.
• Develop statements of work, concepts of operations, requirements, technical approaches, levels of effort, resource plans, and delivery schedules.
• Facilitate meetings and workshops that advance concepts into products or services.
• Define the analytical, geospatial intelligence, digital twin data, and visualization approaches used within each solution.
• Develop technical content for proposals, presentations, and pursuit teams.
• Support client-facing consulting engagements from initial concept and scoping through implementation and delivery.
• Lead the Data Community of Practice to support knowledge sharing, technical discussions, capability development, data product practices, data quality, and medallion architecture principles.
• Establish analytical standards, validation methods, governance practices, and model lifecycle processes.
• Communicate technical architectures, data structures, solution benefits, limitations, and recommendations in clear business terms.
• Present digital solution concepts and technical strategies to executive leadership and client stakeholders.
• Mentor architects and engineers in applied data science, model deployment, geospatial analysis, time-series forecasting, digital twin data workflows, and production-grade data practices.
• Provide technical leadership across multidisciplinary project teams without direct supervisory responsibility.

Benefits

• Employee-owned firm
• Apply your work to problems that matter.
• Bring your own ideas forward.
• Influence enterprise technology strategy.
• Lead through technical expertise.
• Share in what you help build.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior