This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description As a member of the HOPE Team, the Data Lead will partner with the current team to take joint ownership of the HOPE team’s data-related processes and lead in developing and using innovative evaluations, practices, and tools. - Respond to time-sensitive and high-level requests for data related to the Strategic Framework to Prevent and End Homelessness. - Socialize data definitions and develop a shared vision for quality data. - Establish reporting policies and procedures to ensure timely and clean data. - Produce products that facilitate data-driven decision making. - Promote the importance of creating and using data for continuous quality improvement methods and system change efforts. Qualifications - Bachelor’s degree in statistics, data or quantitative analysis, public policy, business, public administration, sociology, or directly related field. - Five years of professional program administration and/or analytical experience in area of assignment. - Two years of supervisory experience. - Significant experience in statistical analyses, data reporting from complex systems, and building tools/dashboards in business intelligence software, like Power BI or Tableau. - Knowledge and experience with service delivery documentation, like HUD standards, HIPAA/Confidentiality standards, utilization review, and data management. Requirements - Refine HOPE team’s data strategy by examining gaps, opportunities, and best practices from other cities. - Manage high-leverage projects and reports that are time-sensitive and essential to the Strategic Framework to Prevent and End Homelessness. - Present major findings to key stakeholder groups inclusive of City leaders, homelessness providers, partners in other sectors, and the public at large. - Ensure the by-name dataset is accurate, timely, and comprehensive by creating data insights and developing policies and procedures. - Design programmatic evaluations that reveal focus areas leading to robust conversations on areas of improvement. - Collaborate with the HOPE team to review strategic planning towards system-level problems. - Develop internal data tools that support staff in efficiently processing the data to better understand their work towards key measures. - Problem-solve data issues from a systems perspective by leveraging knowledge of HMIS to identify trends or anomalies in data. - Inform policies and procedures to clear the path for stronger data infrastructure. - Evangelize the use of data for improvement, advocacy, and storytelling. - Engage with Built for Zero to develop a strong understanding of quality data definitions. - Analyze data and produce data products including monthly inflow/outflow reports and Built for Zero metrics. - Identify and develop plans to fill community data capacity/capability gaps through training and advocating for data resources. Company Description

• Lead the Experimentation Roadmap: Define, champion, and execute a strategic roadmap for measuring impact across PandaDoc, focusing on high-leverage business questions related to customer workflows, churn risk, and long-term value (LTV). • Advanced Experiment Design: Design, implement, and rigorously analyze complex A/B tests, multivariate experiments, and adaptive experimentation methods, including the application of Bayesian experimentation, to assess the effectiveness of proposed product changes and business levers. • Causal Inference Beyond A/B: Apply advanced causal inference techniques (e.g., difference-in-differences, synthetic control, propensity score matching, and instrumental variables) to scenarios where randomized controlled trials (RCTs) are infeasible. • Deep Dive Analysis: Conduct complex, proactive, and exploratory analysis to discover latent user behavior, emerging trends, and root causes of changes in key metrics, translating these findings into actionable product and business insights. • Develop Measurement Frameworks: Define, instrument, and govern a unified Key Performance Indicator (KPI) framework that maps low-level product health metrics to high-level business outcomes, ensuring consistent and scalable measurement across the organization. • Scaling Data Science: Partner with Data Engineering to design and build scalable, self-serve experimentation tooling and reusable analytical assets and frameworks (e.g., causal machine learning models) that empower other analysts and data consumers. • Strategic Influence: Act as a strategic thinker by translating complex statistical findings into clear, compelling, and actionable business narratives for cross-functional partners and senior leadership (VP/C-suite), driving strategic decisions and investment priorities. • Mentorship and Training: Serve as a technical subject matter expert, training and mentoring junior and mid-level data scientists on best practices in statistical rigor, experimental design, and causal modeling.

• Define, champion, and execute a strategic roadmap for measuring impact across PandaDoc, focusing on high-leverage business questions related to customer workflows, churn risk, and long-term value (LTV). • Design, implement, and rigorously analyze complex A/B tests, multivariate experiments, and adaptive experimentation methods, including the application of Bayesian experimentation, to assess the effectiveness of proposed product changes and business levers. • Apply advanced causal inference techniques (e.g., difference-in-differences, synthetic control, propensity score matching, and instrumental variables) to scenarios where randomized controlled trials (RCTs) are infeasible. • Conduct complex, proactive, and exploratory analysis to discover latent user behavior, emerging trends, and root causes of changes in key metrics, translating these findings into actionable product and business insights. • Define, instrument, and govern a unified Key Performance Indicator (KPI) framework that maps low-level product health metrics to high-level business outcomes, ensuring consistent and scalable measurement across the organization. • Partner with Data Engineering to design and build scalable, self-serve experimentation tooling and reusable analytical assets and frameworks (e.g., causal machine learning models) that empower other analysts and data consumers. • Act as a strategic thinker by translating complex statistical findings into clear, compelling, and actionable business narratives for cross-functional partners and senior leadership (VP/C-suite), driving strategic decisions and investment priorities. • Serve as a technical subject matter expert, training and mentoring junior and mid-level data scientists on best practices in statistical rigor, experimental design, and causal modeling.

Overview As a Data Scientist Intern, you will contribute to the development and application of advanced analytics and machine learning methods to solve healthcare informatics challenges, with a particular emphasis on Generative AI and Artificial General Intelligence. You will collaborate with cross-disciplinary teams to build, evaluate, and deploy data-driven solutions that enhance healthcare treatment, operations, and payment systems. This internship offers hands-on experience in statistical modeling, data engineering, and AI model development in a mission-driven environment. Responsibilities - Technical Expertise - Analyze large and complex healthcare datasets to extract actionable insights - Develop, evaluate, and improve machine learning (ML) and deep learning (DL) models, especially LLMs and generative architectures - Design, build, and optimize data pipelines using cloud services (Databricks/AWS/Azure) for scalable ML solutions - Contribute to the development and maintenance of AI governance frameworks and best practices - Implement and manage database architectures, including vector databases and data embeddings - Communication & Collaboration - Work closely with researchers, product managers, and strategic analysts on data and AI related projects - Communicate technical concepts and findings effectively through presentations and written materials to both technical and non-technical stakeholders - Leadership & Management - Take a proactive role in defining analytical project objectives and contributing to solution delivery - Assist in coordinating data science and technical project activities, supporting team goals and daily operations - Collaborate with academic and industry partners on joint research, pilot projects, and internships - External Relationships & Partnerships - Supports vendor evaluations and contributes to collaborations with vendors. - Assists as a liaison between academic institutions, professional organizations, and research groups as needed. - Other - Complete all responsibilities and goals outlined in the internship program. - Complete all special projects and other duties as assigned. - Must be able to perform duties with or without reasonable accommodation. This job description is intended to describe the general nature and level of work being performed in this internship. It is not an exhaustive list of responsibilities, duties, and skills required and does not constitute an employment agreement. This job description is subject to change as Cotiviti’s needs and requirements of the internship evolve Qualifications - Currently pursuing a Bachelor's/Master's/PhD degree in Data Science, Statistics, Computer Science, Biostatistics, or a related quantitative field - Demonstrated interest or experience in AI, healthcare technology, or informatics research. - Experience working with platforms like Databricks, AWS, Azure, or OCI. - Strong programming skills (Python preferred) - Strong skills working with databases and models (ETL, hosting, etc.) - Strong grasp of statistical analysis, machine learning, and data visualization - either through experience or coursework/projects. - Genuine interest in applying data science to healthcare challenges. - Knowledge of healthcare data standards (such as HL7, FHIR, or ICD-10) is a plus but not required - Ability to work collaboratively and communicate effectively with cross-functional teams. Working Conditions and Physical Requirements - Remaining in a stationary position, often standing or sitting for prolonged periods. - Communicating with others to exchange information. - Repeating motions that may include the wrists, hands and/or fingers. - Assessing the accuracy, neatness and thoroughness of the work assigned. - No adverse environmental conditions expected. - Must be able to provide a dedicated, secure work area. - Must be able to provide high-speed internet access / connectivity and office setup and maintenance. Base compensation ranges from $21.00 to $26.00 per hour. Specific offers are determined by various factors, such as experience, education, skills, certifications, and other business needs. Nonexempt employees are eligible to receive overtime pay for hours worked in excess of 40 hours in a given week, or as otherwise required by applicable state law. Date of posting: 2/3/2026 Applications are assessed on a rolling basis. We anticipate that the application window will close on 4/3/2026, but the application window may change depending on the volume of applications received or close immediately if a qualified candidate is selected. #LI-LL1 #LI-remote #intern #LI-DN!