Data Scientist

Calgary Homeless Foundation (CHF)

Calgary Homeless Foundation guides the fight against homelessness. Fueled by this purpose, we envision the day when homelessness is rare, brief, and non-recurring—perhaps an episode in someone's life, but never a condition that defines it. Working in alignment with governments and collaboratively with service providers and community partners, we help translate complex system needs into coordinated, efficient ground-level action that maximizes the impact of every resource and creates lasting pathways out of homelessness. 

 

We are uniquely positioned to observe the many challenges facing our city and strengthen the entire sector through strategic leadership, forward-thinking capacity development, and hands-on, day-to-day support.  

Our comprehensive data warehouse reveals the full picture of homelessness in our community, enabling us to anticipate challenges and identify effective solutions. And we unite diverse stakeholders around shared goals and help address complex situations that no single agency can solve alone.  

Our collective approach and focus on learning and evaluation equip us to address homelessness across our city while keeping the people experiencing it at the heart of our work.  When the system works better together, more people find their way home.

We are looking for passionate, entrepreneurial, and talented people to join our action-oriented, high-impact team.

The Position

Calgary Homeless Foundation (CHF) is seeking a Data Scientist to build and advance our data-science capability, applying advanced analytical methods to better understand homelessness, system dynamics and outcomes and to strengthen evidence-informed planning and decision-making.

CHF maintains person-level and system-level data through its Homeless Management Information System (HMIS) and broader data environment. This data create opportunities to understand pathways into and out of homelessness, patterns of service use, housing outcomes, movement through the homeless-serving system, changing population needs and pressures on system capacity.

The Data Scientist will lead advanced analytical work using longitudinal administrative data, applying statistical modelling, forecasting, segmentation, machine learning and other appropriate methods. The role will develop analytical approaches that help CHF understand complex patterns, anticipate emerging needs, assess outcomes and examine how changes in programs, investments and system conditions may affect the people and communities we serve.

Working alongside Data Analysts, Data Developers, System Planning and teams across CHF, the Data Scientist will translate complex questions into rigorous, reproducible analysis that informs system planning, program design, resource allocation and strategic decision-making. The role will also help establish strong practices for model development, validation and responsible use as CHF's data environment and analytical needs continue to evolve.

Our Staff

CHF staff are action-oriented individuals who are catalytic leaders, courageous collaborators, evidence-inspired, and vision-dedicated. They choose to bring their professional expertise and personal talents to the non-profit sector, to add value to the full community. They work cooperatively with others in a strong team environment; demonstrate flexibility in organizing and undertaking work; show a high degree of initiative, discernment and resourcefulness; exhibit excellent communication and relational skills; demonstrate thoughtfulness and intelligence in decision making; and are focused on creating positive outcomes for persons experiencing homelessness.

How We Work Here

At CHF our approach is grounded in purpose, collaboration, and accountability. We believe that how we work together matters as much as what we achieve.

Here’s what you can expect from us—and what we expect from you:

• Ownership Under Pressure: We take responsibility for outcomes, even when challenges arise.

• Constructive Feedback: We give and receive feedback openly, with the goal of learning and improving.

• Pace with Purpose: We move quickly when needed, without sacrificing quality or integrity.

• Healthy Disagreement: We challenge ideas respectfully and invite discussion to make decisions stronger.

• Shared Understanding: Expertise means building clarity for others, not being “right by default.”

Our culture values transparency, inclusion, and resilience. We name real pressures—external and internal— and navigate them together. Interviews and development conversations focus on real examples of how we act when it’s hard, because that’s when our values matter most.

Accountability and Deliverables 

Advanced Statistical Modelling & Data Science — 30%

• Lead the development and application of advanced statistical and computational methods to complex homelessness and system-planning questions.
• Develop longitudinal analyses and models to better understand pathways into, through and out of homelessness, including patterns of chronicity, housing stability and returns to homelessness.
• Develop population segmentation and cohort methodologies to identify meaningful differences in needs, service utilization, system interactions and outcomes.
• Develop, test and validate predictive and machine-learning models where there is a clearly defined, responsible and actionable use case.
• Apply statistical methods appropriate to the question and available data, including regression, survival/time-to-event analysis, classification, clustering and other relevant approaches.
• Examine relationships among client characteristics, service interactions, system conditions and outcomes while appropriately accounting for the limitations of observational administrative data.
• Translate complex organizational and system questions into testable analytical questions, appropriate methodologies and clearly defined analytical outputs.

Forecasting, System Modelling & Scenario Analysis — 25%

• Develop forecasting models to understand potential changes in active homelessness, inflows and outflows, housing placements, service demand and system capacity.
• Model movement through the homeless-serving system to identify transitions, recurring patterns and potential bottlenecks across populations, programs and services.
• Develop scenario models to examine the potential effects of changes in investment, capacity, program design, housing availability or other system conditions.
• Quantify uncertainty and key assumptions so decision-makers can understand the range and limitations of potential outcomes.
• Conduct geographic and spatial analysis where it strengthens understanding of service demand, access, population patterns or system capacity.
• Develop modelling approaches that can adapt to additional communities, populations and datasets as CHF's data environment evolves.

Applied Research & Impact Analytics — 20%

• Provide advanced quantitative and methodological expertise for analyses of programs, interventions and system initiatives.
• Develop longitudinal and comparative approaches to understand housing stability, returns to homelessness, chronicity, service utilization and other priority outcomes.
• Work with relevant CHF teams to define appropriate cohorts, comparison groups, outcome measures and analytical designs.
• Apply causal-inference and quasi-experimental methods where the available data and research design support their use.
• Support cost, utilization and impact analyses involving homelessness and adjacent public systems where appropriate data and data-sharing arrangements are available.
• Clearly distinguish descriptive findings, statistical associations and causal evidence and communicate the limitations of each.
• Contribute advanced analytical expertise to research partnerships and cross-sector studies where CHF data or subject-matter expertise is involved.

Model Development, Validation & Responsible Data Science — 15%

• Establish practical standards for the development, testing, validation, documentation and monitoring of statistical and machine-learning models.
• Assess whether available data are appropriate for proposed analytical applications, considering completeness, missingness, representativeness, selection effects and changes in definitions or data-collection practices.
• Evaluate model performance, stability and generalizability before analytical methods are operationalized or used to inform decisions.
• Assess potential bias, fairness, privacy and unintended consequences associated with analytical models, particularly where findings may influence services or decisions affecting individuals.
• Promote interpretable analytical approaches and appropriate human oversight.
• Ensure advanced analytical work aligns with CHF's privacy, data-governance and information-security requirements.

Analytical Integration & Reproducibility — 5%

• Develop reproducible analytical workflows using Python and/or R, SQL and appropriate data-science technologies.
• Partner with Data Developers to define analytical datasets, features, longitudinal structures and person-level linkage requirements needed for advanced analysis.
• Work with Data Developers to transition validated models or analytical methods into reliable, repeatable workflows where appropriate.
• Partner with Data Analysts where advanced analytical outputs need to be incorporated into ongoing measurement, reporting or decision-support products.

Strategic Insight & Knowledge Translation — 5%

• Translate advanced analytical findings into clear implications for CHF leadership, System Planning, government partners and other stakeholders.
• Communicate methodology, assumptions, uncertainty and limitations clearly to both technical and non-technical audiences.
• Provide analytical expertise on strategic initiatives and complex government or system-level questions.
• Identify opportunities where advanced analytics can strengthen CHF's understanding of homelessness, system performance and emerging needs.

Education and Experience

Education

A graduate degree in Data Science, Statistics, Biostatistics, Economics, Computer Science, Operations Research, quantitative social science or a related quantitative discipline is preferred. An equivalent combination of education and substantial applied data-science experience will be considered.

Experience

• 5+ years of experience applying advanced statistical and data-science methods to complex real-world datasets.
• Demonstrated experience working with longitudinal, person-level, administrative or similarly complex datasets.
• Demonstrated experience developing and validating statistical, forecasting, segmentation and/or machine-learning models.
• Experience applying quantitative methods to understand outcomes, programs, policies or interventions using observational data.
• Demonstrated ability to translate complex or ambiguous policy, operational or business questions into rigorous analytical approaches.
• Experience assessing the limitations, biases and suitability of real-world data for statistical modelling and decision-making.
• Demonstrated ability to communicate complex analytical findings, assumptions and limitations to non-technical decision-makers.
• Experience working with health, social-service, government, population-level or other sensitive administrative data is an asset.
• Experience working with homelessness or housing data is an asset but is not required.

Technical Capabilities

The successful candidate will bring strong capability in:

• Python and/or R;
• advanced SQL;
• Statistical modelling and inference;
• machine learning and predictive modelling;
• forecasting and time-series analysis;
• longitudinal and cohort analysis;
• model validation and performance assessment;
• reproducible analytical workflows and version control; and
• communicating complex quantitative analysis through effective visualization and presentation.
• Experience with Azure, Databricks, geospatial analytics, record linkage/entity resolution or cloud-based analytical environments is an asse

 

Core Competency Requirements

The ideal candidate will be expected to demonstrate the following core competencies:

• Analytical Judgement: Selects methods based on the question, data and intended use rather than applying techniques because they are available. Recognizes when the available evidence does not support a requested conclusion.
• Problem Formulation: Can take complex or ambiguous system questions and translate them into clearly defined analytical problems that can be investigated with available data.
• Critical Thinking: Challenges assumptions, tests alternative explanations and considers data limitations before drawing conclusions.
• Communication: Explains complex methods, findings, uncertainty and limitations clearly to technical and non-technical audiences.
• Collaboration: Works effectively with Data Analysts, Data Developers, System Planning, HMIS and other teams while bringing a distinct advanced-analytics perspective.
• Responsible Use of Data: Demonstrates sound judgement when working with sensitive person-level data and understands the potential consequences of applying statistical or predictive methods to vulnerable populations.
• Initiative & Learning: Independently investigates complex problems, stays current with relevant analytical methods and identifies where new approaches can create meaningful value for CHF.

General Competency Requirements

The ideal candidate will be expected to demonstrate and grow the following competencies:

Taking Accountability for Guiding the Fight Against Homelessness by:

• Regularly sharing our Purpose and Ambition and making decisions that move us toward
• Following through on my commitments to earn the trust of my colleagues and community partners
• Identifying obstacles, admitting my mistakes, and taking ownership of solutions

Fostering a Growth Mindset by:

• Regularly seeking new information and leveraging data to inform my solutions and decisions
• Treating mistakes as learning opportunities and focusing myself & others on improving future results
• Considering how I may have contributed to problems and regularly seeking feedback to discover how I can improve
• Accepting ambiguity and taking action to move us in the direction of our goals without waiting for perfect information

Empowering Others to Succeed by:

• Articulating and agreeing upon clear expectations and desired results
• Meeting regularly with my key internal and external stakeholders to ensure they have what they need from me to succeed
• Eliminating organizational constraints that hold people back

Collaborating for Greater Impact by:

• Proactively collaborating with others and incorporating their input into solutions
• Respecting the unique strengths and agency of others by encouraging them to propose solutions and make decisions
• Having the courage to share difficult perspectives directly, respectfully, and in a timely manner, and encouraging others to do the same
• Being willing to change my perspective when presented with new information