Data Scientist

• Responsible for mining big data, interpreting and analyzing complex data sets
• Develops and deploys predictive models to analyze company performance and improve customer experiences
• Applies statistical techniques and machine learning algorithms to identify trends within Mazda data
• Collaborates closely with data engineers, analysts, and business stakeholders to integrate data-driven solutions

• Unified reporting and analytics: Actively build and maintain reports and dashboards that give the CS organisation clear visibility into key performance metrics, trends, and performance across channels, teams and projects
• CS Systems configuration and data governance: Work closely with systems admins to advise on the structural setup of our Zendesk instance across customer-facing teams, ensuring it is configured to generate clean, consistent, and reliable data while maintaining secure systems and efficient workflows.
• Data hygiene standards: Define and enforce standards for how data is captured across CS systems so that reporting is accurate, trustworthy, and reproducible
• AI automation data oversight: Monitor and analyse data from our AI chatbot, ensuring it is tracked in a way that supports quality reviews and decision-making across the CS organisation
• Insights to action: Translate raw data and reporting into clear recommendations that help CS leadership make decisions on resourcing, tooling, process improvements, and strategy
• Stakeholder support: Partner with cross-functional partners in areas like Product, Data and Strategic Finance to understand their data needs and deliver reporting that supports their goals
• Systems evaluation: Assess the data capabilities of existing and new CS tools, and make recommendations on how to optimise our systems stack for better reporting outcomes
• Process documentation: Document data structures, reporting methodologies, and system configurations to ensure institutional knowledge is retained and accessible across the team

Customer Support Lead – Systems & Analytics

• Design, develop, and deploy time-series forecasting models including ARIMA, SARIMA, and ETS
• Build machine-learning-based forecasting models using GBM, Random Forest, XGBoost, and LightGBM
• Develop hierarchical and multi-level forecasting solutions across products, regions, and channels
• Perform large-scale data extraction, transformation, and analysis using SQL
• Partner with supply chain, merchandising, planning, and business teams to understand forecasting requirements.
• Translate business problems into analytical solutions.
• Present model insights and recommendations to senior client stakeholders.
• Lead workshops and forecasting strategy discussions with clients.
• Implement and operationalize models in cloud environments
• Collaborate with business stakeholders to translate demand planning requirements into scalable analytics solutions

Senior Data Scientist – Forecasting

• Design and implement advanced ML models and statistical methods to optimize forecasting, risk assessment, and decision-making processes.
• Conduct data provenance tracking, ensuring documentation of sources, transformations, and lineage for compliance with governance policies.
• Submit the Data Provenance & Lineage Report, summarizing transformation workflows, feature engineering processes, and audit compliance.
• Implement sprint-based Agile methodologies, ensuring rapid development cycles, backlog grooming, and alignment with mission requirements.
• Provide a Rough Order of Magnitude (ROM) Estimate Report before each analytics project, detailing expected Full-Time Equivalent (FTE) hours, compute costs, storage consumption, and infrastructure requirements.
• Conduct quarterly reviews to track cost efficiency, assess system performance, and optimize analytic workflows through the Quarterly Cost & Resource Utilization Report.

Senior Data Scientist

• Develop and refine predictive models.
• Conduct exploratory data analysis.
• Generate AI-driven insights to enhance intelligence and operational planning.
• Integrate customer feedback into model iteration cycles, leveraging Agile development methodologies.
• Submit Predictive Model Performance Report, documenting key findings, model accuracy metrics, and operational impact assessments.
• Implement sprint-based Agile methodologies, ensuring rapid development cycles, backlog grooming, and alignment with mission requirements.
• Conduct quarterly reviews to track cost efficiency, assess system performance, and optimize analytic workflows through the Quarterly Cost & Resource Utilization Report.

• Advise clients on end-to-end solutions across data strategy, data management, and data quality.
• Support business development, methodology development, team leadership, and delivery execution for data governance assessments and implementations.
• Design and develop data governance technology solutions across data quality, data dictionaries, business glossaries, data standards, and data policies.
• Define organizational design, governance processes, policies, roles, responsibilities, success metrics, and operating models for client data programs.
• Build roadmaps covering technology, culture, governance, processes, skills, competencies, data quality, analytics, and data management maturity.
• Lead and collaborate with data governance teams to create business cases, resource plans, implementation roadmaps, and enterprise data system assessments.
• Assess current-state environments, identify client pain points and requirements, and help define a practical vision and strategy for becoming a data-driven organization.
• Advise clients as a trusted partner and provide thought leadership on data governance, data management, cloud transformation, AI readiness, and related best practices.
• Manage resources, budgets, project delivery, team development, training, mentoring, and performance assessment.

Data Strategy and Governance Manager

• Ensure that GAIA has the customer content and supporting analytical views and tools to achieve its goals for the creation of foundation models requiring this data
• Help realize additional return on GAIA data investments by customizing our datasets, analytical views, and tools for general use within the company
• Identify customer and third party developer workflows that could leverage our analytical capabilities and tools
• Define requirements, oversee development prioritization, arrange delivery, and drive adoption of the products in your portfolio
• Partner closely with GAIA product management and strategy to ensure your product plan for customer content datasets meets GAIA AI development needs

Senior Technical Product Manager, Data Acquisition

• Designing and building the data Ingestion Pipeline
• Designing, building and maintain large, complex data processing pipelines using Databricks and Azure Data Factory in Azure to meet enterprise Data requirements
• Providing operational and functional support on creating, storing, managing, and maintaining enterprise data
• Identifying, designing, and implement internal process improvements
• Building the infrastructure required for extraction, transformation, and loading of data from a wide variety of cloud and on-premises data sources
• Working with stakeholders including to assist with data-related technical issues and support their data infrastructure needs
• Working closely with data architecture, data governance and data analytics teams to ensure pipelines adhere to enterprise standards, usability, and performance

ADF Lead, Data Bricks, Snowflake

• Collaborate and communicate with all levels of management to provide reporting and critical information.
• Creates analysis and visualization materials for business data, metrics, and measures.
• Designs, validates, and schedules queries and reports in various databases and reporting environments.
• Works closely with end users and management to evolve, improve, and automate existing data processes.
• Develops specifications, documentation, and end user materials.
• Documents and maps existing and desired business processes.
• Deploys reports, SQL database objects, and applications to on-premises and cloud environments.

Enterprise Data Lead Analyst

• Define Ads ontologies and metrics for campaigns, budgets, bids, creatives, audiences, and placements.
• Build dbt models and curated marts in Snowflake with clear data contracts, tests, and SLOs.
• Ingest and enrich unstructured Ads content and publish retrieval‑ready datasets using our managed search/vector services.
• Design and evaluate retrieval workflows (RAG) with existing services for hybrid search and re‑ranking; set quality/latency targets and iterate via experiments.
• Design agent reasoning and policies on ads, including tool definitions and human‑in‑the‑loop approvals.
• Establish evaluation suites covering precision/recall, calibration, hallucination rate, latency, and cost.
• Run A/B or uplift experiments to quantify impact and guide iteration.
• Translate Ads problems into agent behaviors and own KPIs such as ROAS lift, pacing accuracy, RCA precision/recall, forecast MAPE, and time‑to‑insight.

Ads AI Analytics Lead

• Own clinical data quality across claims, EHR, and ADT: Define standards for how clinical data is structured, normalized, and validated as modeling inputs across payer claims (medical, pharmacy, eligibility), EHR data (Epic, Cerner, Athena), and real-time ADT feeds. Bring deep familiarity with EHR data formats (FHIR, HL7, C-CDA) and how data from systems like Epic, Cerner, and Athena maps to clinical reality. Hold the bar for clinical accuracy and completeness across all three sources.
• Build and ship production ML/AI models: Develop, validate, and deploy risk stratification, care gap prediction, treatment effect estimation, and LLM/foundation model applications — with rigor around leakage, calibration, fairness, and clinical face validity.
• Apply health economics and outcomes methods: Translate raw clinical and claims data into decision-grade evidence through risk adjustment, utilization measurement, cost attribution, quasi-experimental evaluation, and outcomes measurement aligned with CMS, NCQA, and MCO reporting standards.
• Advance machine and AI products: Bring senior modeling judgment to the product roadmap, owning the clinical and methodological soundness of what ships.
• Set standards and mentor: Make architectural trade-offs, drive alignment across data science, engineering, product, and clinical stakeholders, and mentor junior data scientists to raise the technical bar of the team.

Principal Data Scientist, Health Informatics

• Partner with Marketing to Define and Solve Problems – Work closely with marketing stakeholders to understand business challenges, define success metrics, and translate needs into analytical approaches that drive performance across campaigns and channels.
• Design and Deliver Data-Driven Solutions – Apply statistical analysis and machine learning to develop solutions that address business needs, then present findings, influence decisions, and gain alignment on adoption.
• Lead Experimentation and Optimization – Develop and manage testing frameworks (A/B testing, campaign experimentation) across channels and markets. Analyze results and provide clear recommendations to improve performance and inform future strategy.
• Translate Results into Business Impact – Clearly communicate insights and quantify outcomes (e.g., campaign performance lift, engagement improvements, ROI) to ensure stakeholders understand the value and take action.
• Partner to Operationalize Solutions – Collaborate with Technology and Engineering teams to transition validated models into production, supporting implementation through scalable pipelines and processes.
• Influence Through Storytelling – Present insights and recommendations to both technical and non-technical audiences, including senior stakeholders, to drive alignment and decision-making.

Senior Data Scientist – Marketing Analytics

• Develop research methods and data analysis plans related to healthcare utilization, cost trends, financial forecasting, member, and provider experience.
• Expertly use appropriate software tools to retrieve, edit and tabulate data from various databases and files.
• Conduct descriptive, prescriptive, predictive analyses.
• Summarize complex data for leadership in the appropriate form (e.g., statistical summaries, narratives, charts, tables, graphs, etc.) to convey meaning, significance, and actionable information.
• Contribute/Lead in the research and development of models, algorithms, and analysis tools to mature the use and creation of self-service data products at CareOregon.
• Develop visual reporting (e.g., dashboards, scorecards, etc.) using data visualization tools such as Tableau when appropriate.
• Develop appropriate subject matter, domain expertise and serve as an expert resource.
• Collaborate with other subject matter experts to identify opportunities for building repeatable data products.
• Collaborate with business teams to understand business needs and to illicit and document requirements for analytics, algorithms, models, etc.
• Develop reporting tools to comply with regulatory reporting requirements.
• Review and assess completeness, validity, and reliability of internal data used for analysis and reporting.
• Create and maintain technical documentation such as data dictionaries, business glossaries, metric definitions, and other data resources.
• Thoroughly document models, algorithms, data products, processes and any analysis, reports developed.
• Contribute to, and actively participate in, community of practice to improve the skills and capabilities of other analysts at CareOregon.
• Assist with, prepare, and/or deliver presentations to community groups, regulatory agencies, leadership, and other teams as needed.

Data Scientist jobs in Oregon

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Data Scientist

Customer Support Lead – Systems & Analytics

Senior Data Scientist – Forecasting

Senior Data Scientist

Data Scientist

Data Strategy and Governance Manager

Senior Technical Product Manager, Data Acquisition

ADF Lead, Data Bricks, Snowflake

Enterprise Data Lead Analyst

Ads AI Analytics Lead

Principal Data Scientist, Health Informatics

Senior Data Scientist – Marketing Analytics

Data Science Analyst II