Data Scientist

What Success Looks Like In This Job

The Assessment Data Scientist serves as the Assessor’s Office senior data science expert, leading the design, implementation, and oversight of advanced Computer-Assisted Mass Appraisal (CAMA) and Automated Valuation Models (AVMs). This role applies predictive analytics, statistical modeling, and machine learning to improve the accuracy, transparency, and efficiency of property valuations in alignment with statutory deadlines, Division of Property Taxation guidelines, and IAAO standards. The position ensures equitable valuation by identifying market trends, detecting anomalies, and improving data quality through collaboration with appraisal, GIS, and IT teams. Additionally, the role translates complex modeling results into clear, actionable insights for leadership, stakeholders, and the public, while mentoring staff to build organizational capacity in advanced analytics.

Examples of Duties for Success

• Serve as a senior-level data science and modeling expert in the Assessor’s Office, responsible for developing and managing advanced Computer-Assisted Mass Appraisal (CAMA) and automated valuation models (AVMs).
• Design and implement predictive analytics and machine learning models to enhance accuracy, transparency, and efficiency in property valuation processes.
• Ensure compliance with statutory property valuation deadlines, Division of Property Taxation (DPT) guidelines, and International Association of Assessing Officers (IAAO) standards through data-driven modeling and quality control.
• Lead the development of scalable statistical and machine learning solutions to identify market trends, detect anomalies, and support equitable valuation of all property types.
• Collaborate with appraisal, GIS, and IT teams to improve data quality, leverage automation, and integrate cutting-edge analytics into existing workflows.
• Translate complex modeling outputs into actionable insights for leadership, stakeholders, and the public.
• Develop, implement, and maintain advanced AVMs and CAMA models using multiple regression, machine learning algorithms, and predictive analytics techniques.
• Perform ratio studies, time trend analysis, and depreciation modeling using both traditional statistical methods and advanced AI-driven approaches.
• Analyze large-scale datasets from CAMA systems, GIS platforms, and external sources to uncover patterns, drivers of value, and predictive variables.
• Build and deploy interactive dashboards and visual analytics tools (Tableau, Power BI) to communicate key insights and support decision-making.
• Monitor evolving Division of Property Taxation (DPT) guidelines, IAAO standards, and emerging technologies to ensure compliance and maintain innovation in valuation practices.
• Implement continuous model performance testing, error analysis, and recalibration to improve predictive accuracy and reduce bias.
• Support automation strategies and process improvements for mass appraisal through scripting, APIs, and integration with enterprise systems.
• Train and mentor staff on data literacy, advanced analytics, and machine learning applications in property valuation.
• Present modeling outcomes and predictive insights to management and public stakeholders in a clear, accessible format.
• Perform other related duties as assigned to enhance data-driven operations within the Assessor’s Office.

Qualifications for Success

Knowledge Of:

• Advanced statistical modeling, predictive analytics, and machine learning concepts applied to mass appraisal and property valuation.
• Automated Valuation Models (AVMs), CAMA systems, and modern data science methodologies.
• Programming languages and analytics tools such as Python, R, SQL, and relevant machine learning libraries (e.g., scikit-learn, TensorFlow).
• Geographic Information Systems (GIS) and spatial analytics tools (e.g., ESRI ArcGIS).
• Data visualization and reporting platforms such as Tableau or Power BI for business intelligence and decision support.
• Industry standards and ethics set by the International Association of Assessing Officers (IAAO) and state property assessment regulations.

Skill To:

• Design and implement machine learning models, predictive algorithms, and regression-based valuation models for large datasets.
• Leverage automation and scripting (e.g., Python, R) to streamline workflows and improve operational efficiency.
• Develop interactive dashboards, predictive tools, and advanced analytics visualizations.
• Communicate complex technical results clearly to leadership, staff, and non-technical stakeholders.

Ability To:

• Apply advanced data science techniques to real-world property valuation challenges.
• Lead data-driven initiatives and mentor staff on modern analytics and modeling practices.
• Work under pressure to meet statutory deadlines without compromising model accuracy and compliance.
• Build cross-functional partnerships with appraisal, GIS, and IT teams to deliver innovative solutions.
• Understand, interpret, and communicate state rules and regulations and tax code guidelines.
• Translate technical concepts into actionable business strategies for public transparency and equity in valuation.

More Qualifications for Success

Any combination of experience and training that would likely provide the required knowledge and abilities is qualifying. A typical way to obtain the knowledge and abilities would be:

Experience: Six (6) years of increasingly responsible related data collection, process analysis/mapping, research, and statistical analysis experience, including a minimum of two (2) years’ related work experience in property tax assessment or a similar field.

Education and Training:

• Bachelor’s degree in Data Science, Statistics, Mathematics, Economics, Computer Science, or a related field.
• A Master’s degree is preferred and may substitute 2 years’ required experience, excluding work in property tax assessment.

License or Certificate: None.

Background Check: Must pass a criminal background check.

Other: State Residency: At the time of appointment, candidate must be a legal resident of the state of Colorado.