Data Scientist

• Design analytical solutions that harness internal and external data, leveraging visualizations, predictive analytics, and prescriptive methods.
• Collaborate with partners in purchasing, product development, manufacturing, warranty, material planning, and logistics to define problems, identify data, and develop machine learning algorithms.
• Evaluate large, complex data sets using SQL, Hadoop, Alteryx, Python, R, and sophisticated statistical and machine learning techniques.
• Develop business solutions using data acquisition, data engineering, programming, and visualization skills, designing intuitive user interfaces.
• Ensure the overall quality of data solutions throughout the analytic development process.

Data Scientist – Supply Chain Analytics

• Develop and deploy advanced analytics frameworks to identify anomalous vehicle behaviors, diagnostic trends, and emerging quality risks across millions of connected vehicles.
• Apply statistical methods, machine learning, and AI-driven detection techniques to improve early warning capabilities and reduce time-to-detection for vehicle quality issues.
• Design scalable algorithms and data models capable of analyzing billions of telemetry and diagnostic data points across vehicle platforms.
• Lead enterprise use of connected vehicle data throughout the product lifecycle, including pre-production validation, launch monitoring, and in-market quality surveillance.
• Define strategies for leveraging cloud-based telemetry, diagnostic, and software interaction data to improve product quality outcomes.
• Drive continuous improvement of detection logic, escalation frameworks, and data-driven quality processes.
• Lead use of connected data for quality across the entire product lifecycle, from TT builds in CVLQ to MP1 builds in EWRT and post-OKTB data in UCS.
• Lead deep-dive investigations into software, hardware, calibration, and systems integration issues using connected vehicle data.
• Correlate signals across vehicle functional domains to identify root causes and systemic quality trends.
• Partner with Product Development, Software, Hardware, Manufacturing, and Quality teams to drive issue resolution and containment actions.
• Drive development of automated analytics pipelines, AI-enabled monitoring tools, and executive dashboards supporting enterprise quality initiatives.
• Partner with data engineering and platform teams to improve data accessibility, reliability, and scalability across cloud environments.
• Champion modern data science methodologies and operational analytics best practices within the organization.
• Lead cross-functional initiatives focused on launch quality improvement, issue prevention, and reduction of customer-impacting failures.
• Translate highly technical findings into concise executive-level insights and strategic recommendations.
• Influence decision-making across engineering, quality, and leadership organizations without direct authority.

Vehicle Connected Data Manager

• Design, develop, validate, and deploy machine learning and AI solutions for business-critical applications.
• Build scalable predictive models, anomaly detection systems, forecasting solutions, recommendation systems, and generative AI applications.
• Develop conversational AI and agent-assist solutions leveraging LLMs, NLP, and retrieval-augmented generation (RAG) techniques.
• Create intelligent AI agents for business workflow automation and SDLC acceleration initiatives.
• Develop and optimize fraud detection models using supervised and unsupervised machine learning techniques.
• Analyze structured and unstructured datasets to identify trends, patterns, risks, and business opportunities.
• Partner with engineering teams to productionize AI/ML solutions and integrate them into enterprise applications and workflows.
• Develop reusable ML pipelines, feature engineering frameworks, and model monitoring capabilities.
• Monitor model performance, drift, reliability, and operational effectiveness in production environments.
• Collaborate with product managers, engineers, business stakeholders, and risk/compliance teams to define requirements, success metrics, and implementation strategies.
• Translate technical insights and analytical findings into clear business recommendations and executive-level communications.
• Ensure AI and machine learning solutions comply with data governance, privacy, security, and regulatory standards.
• Develop documentation supporting model explainability, validation, monitoring, and audit readiness.
• Promote responsible AI practices, including fairness, transparency, and risk mitigation.
• Mentor junior team members and contribute to technical standards, best practices, and continuous improvement initiatives.

Senior Data Scientist

• Develop and deliver high-impact analyses, dashboards, and reports to track marketing performance, campaign ROI, funnel metrics, and customer behavior
• Build intuitive, self-serve visualizations using tools like Power BI to support marketers in tracking their KPIs
• Develop and maintain scalable data models, ensuring data integrity and accuracy in reporting
• Serve as a trusted analytics advisor to marketing leaders and channel owners, providing data-driven recommendations to optimize campaigns and programs
• Contribute to the development of measurement frameworks, attribution models, and marketing mix optimization
• Lead/participate in cross-functional collaboration with data engineering, digital, and product teams to ensure analytics solutions are scalable and aligned with business needs

Marketing Analytics Lead

• Serve as the primary point of contact for At Work-related data and analytics needs
• Partner with CAPTRUST’s centralized data team to prioritize, design, and improve data solutions supporting At Work
• Help establish consistent definitions, metrics, and reporting standards across the At Work practice
• Improve data quality, clarity, and usability over time
• Support and enhance internal and client-facing reporting used by At Work teams
• Work closely with Operations to understand recurring reporting needs and reduce one-off or manual efforts
• Develop dashboards and reports that support operational planning, advisor effectiveness, and leadership decision-making
• Translate reporting outputs into insights that inform action, not just describe activity
• Develop qualitative and quantitative measures of client and participant engagement, trust, and relationship health
• Help establish ongoing feedback loops across the client and participant lifecycle, beyond episodic surveys
• Identify trends that signal strengthening relationships, stagnation, or potential breakdowns
• Support analysis related to the annual financial wellness survey and other experience-related initiatives
• Analyze patterns associated with strong long-term outcomes such as sustained engagement, retention, and expansion of advisory relationships as needs evolve
• Partner with leadership and Operations to identify where additional investment of time or resources may be needed
• Help reduce reliance on anecdotal feedback through clearer, shared insight

Data & Analytics Lead

• Develop and deliver high-impact analyses, dashboards, and reports to track marketing performance, campaign ROI, funnel metrics, and customer behavior.
• Build intuitive, self-serve visualizations using tools like Power BI to support marketers in tracking their KPIs.
• Develop and maintain scalable data models, ensuring data integrity and accuracy in reporting.
• Serve as a trusted analytics advisor to marketing leaders and channel owners, providing data-driven recommendations to optimize campaigns and programs.
• Contribute to the development of measurement frameworks, attribution models, and marketing mix optimization.
• Lead/participate in cross-functional collaboration with data engineering, digital, and product teams to ensure analytics solutions are scalable and aligned with business needs.

• Develop and deploy Natural Language Processing (NLP) models to extract insights from unstructured textual data.
• Collaborate with cross-functional teams to identify opportunities and develop strategies for applying NLP techniques to enhance quality analytics.
• Design and implement data pre-processing and feature engineering techniques for NLP tasks.
• Utilize supervised and unsupervised machine learning techniques to solve complex NLP problems.
• Evaluate and fine-tune models for performance optimization, accuracy, and efficiency.
• Contribute maintainable code to existing and new pipelines.
• Stay up-to-date with the latest advancements in NLP and contribute to the continuous improvement of methodologies and algorithms.
• Communicate findings, insights, and recommendations to stakeholders in a clear and concise manner.

• Define and manage data collection requirements for the ADAS organization, addressing both field issues and the development of innovative new features.
• Collaborate with Ford’s Connected Vehicle Data Enablement (CVDE) team to implement custom data collection strategies.
• Analyze large-scale datasets in GCP using BigQuery (SQL) and Python.
• Develop data products and Machine Learning models by fusing multi-domain sources, including CVDE data, warranty claims, customer verbatims, weather, and road/lane geometry.
• Execute ML model inference and perform sensitivity analysis on data products to develop a deep understanding of the data.
• Democratize insights across the organization through automated “Push Analytics.”
• Build Text-to-SQL and code-generation/execution AI tools to enable “Custom Pull Analytics,” allowing Subject Matter Experts (SMEs) to retrieve bespoke insights.
• Develop interactive AI/ML applications using the Python ecosystem (e.g., Chainlit, Dash, or Streamlit) and design dashboards in Superset, PowerBI, or Looker Studio for standardized reporting.

Applied Data Scientist

• Design, build, and own end-to-end machine learning models and AI-powered solutions, from problem framing through production deployment and impact measurement, focused on driving customer retention and operational excellence
• Develop and enhance AI-driven tools that deliver explainable, actionable insights to Customer Success and Renewals teams, enabling data-informed decisions that protect and grow revenue
• Partner closely with Customer Success, Renewals, Product, and Engineering to translate complex business challenges into well-scoped data science initiatives aligned to high-impact retention opportunities
• Evaluate, prototype, and implement emerging AI/ML approaches, including generative AI and agentic workflows, to improve team efficiency and stakeholder value in a rapidly evolving landscape
• Mentor and support fellow Data Scientists by setting high standards for analytical rigor, code quality, and documentation while contributing to best practices and team growth

Senior Data Scientist, Customer Care

• Work from Anywhere as a Senior Revenue Cycle Analytics & Data Science Expert.
• Leverage data science to tackle operational challenges like denials management and AR optimization.
• Develop, test, and deploy standard reports and certified datasets to drive accountability.

Senior Revenue Cycle Analytics, Data Science Expert

• Lead the Journeys & COE Consulting Portfolio.
• Deliver Strategic Consulting & Insights.
• Enable COEs in the Shift to Journey-Centric Insights.
• Connect Journeys with the Integrated People Analytics Team Operating Model.
• Strengthen Ways of Working & Governance.
• Influence, Enable, and Build Data Literacy.

People Analytics Leader, Journeys and COE Consulting

• Gather, preprocess, and validate datasets from internal and external sources.
• Identify trends, patterns, and anomalies using statistical and visual techniques.
• Build and evaluate predictive models using machine learning algorithms.
• Create dashboards and visual reports to communicate findings to stakeholders.
• Maintain clear records of methodologies, code, and project outcomes.
• Work closely with cross-functional teams to align data solutions with business needs.

Data Science, AI Intern

• Collaborate with leaders across the organization to develop and execute on a data strategy inline with ActBlue's near and long term goals, including supporting organizational growth through acquisitions and partnerships
• Foster a data centric culture by upleveling our ability to make data-informed decisions and build best in class data products that meet rigorous performance and reliability standards
• Enable and advocate for AI/ML driven products and features across the ActBlue Platform through building scalable machine learning infrastructure that serves production environments and identifying new opportunities to deploy data science and analytics capacity in service of our mission
• Oversee our Data Science, Data Analytics, Data Engineering, and Data Platform teams in a fast-paced, mission-driven environment
• Guide the evolution of our data systems, tools, and architecture to meet the organization's needs, including data consolidation, governance frameworks, and unified data models across multiple entities
• Ensure high-quality communication and a stakeholder-centric approach between our data teams and their internal customers, with an emphasis on responsiveness and partnership during high-stakes moments

Director of Data Science – Engineering

• As a Data Scientist – Modeling and Analytics, you will be responsible for creating statistical models and performing analyses that drive sales and policy growth for the organization.
• You will partner with marketing team members, marketing managers, and other data analysts and scientists to identify business needs, gather data, build, and maintain effective models, and assess model performance over time.
• Build, maintain, and automate models to predict purchase propensity, policy premium, policy lapse/retention, cross-selling, upselling, next best action, and other consumer behaviors using both internal data, census data, appended aggregated data, and macroeconomic data.
• Recommend marketing distribution strategies leveraging data and models.
• Conduct advanced exploratory data analysis.
• Perform model interpretability and explainability analysis.
• Leverage specific metrics for model performance evaluation (e.g., precision, recall, F1 score).
• Implement A/B testing and experimental design and quantitative benchmarks for model improvement.
• Apply data privacy and compliance rules under regulations like GDPR, CCPA.
• Apply ethical AI principles.
• Apply model fairness and bias mitigation techniques.
• Conduct analyses to assess model performance and campaign performance, both against test datasets and actual results once deployed.
• Forecast campaign results based on models built and validate forecast against actuals.
• Work with marketing data architects and engineers to ensure data is clean, complete, correct, and suitable for modeling using AI/ML platforms.
• Develop and maintain data pipelines.
• Implement feature engineering techniques.
• Find, recommend, and purchase additional data to use in model building.
• Proactively identify opportunities for model improvement and need for additional modeling projects.
• Maintain clear and organized documentation of data, methodologies, and results.
• Implement automation in existing processes to improve overall efficiency.
• Perform ad hoc analysis to support Marketing Distribution efforts.
• Actively seek out innovation and optimization use cases and experiments that will result in organizational transformation and sales and profit improvements.

Data Scientist – Modeling and Analytics

• Own the full data science lifecycle
• Partner with Product Teams to enhance product value
• Analyze complex business problems to identify margin opportunities
• Bridge the gap between complex model outputs and business operations
• Translate complex statistical findings into clear narratives and actionable insights

• Own the proprietary model flywheel with 90M+ assigned programs and build a proprietary therapeutic AI model
• Lead AI product delivery across all AI bets and drive design, development, deployment, and continuous improvement of AI systems
• Define and execute the AI/ML technical roadmap by collaborating with Product and Content to prioritize clinical and product needs
• Establish trust-by-design as a standard by ensuring transparency and auditability in all AI features
• Own the clinical AI safety and quality framework, ensuring HIPAA-compliant data handling alongside Legal
• Drive cross-functional alignment and work closely with various teams to embed AI in workflows
• Continuously evaluate emerging developments in LLMs and clinical AI for practical advantages

Head of Data and Machine Learning

• Bridge the gap between complex datasets and strategic action.
• Synthesize information from various external sources to develop visibility and insights into search recall gaps and emerging topic trends.
• Inform Wikimedia’s strategy for ensuring that people continue to find the content they need.
• Integrate fragmented data to create a unified view of how users discover and use content.
• Translate analytics and complex trends into actionable narratives.
• Develop automated systems to detect under-indexed content.
• Establish metrics and frameworks for content utilization within Large Language Models.
• Collaborate with product leaders to set goals and track success.
• Assess efficacy of new initiatives through experiment design and analysis
• Interpret website performance data to inform UX/UI enhancements.

Senior Data Scientist, Ecosystems

• Develop & Sustain Power BI dashboards
• Develop and sustain AI agents
• Identify newer technologies team should be using and cross train others
• Develop and automate manual processes
• Collaborate cross-functionally to source and prioritize data effectively
• Utilize tools such as Partsmart (PIM), Erapa, ARC, Databricks, Knime, Power BI, Access databases, and SQL
• Develop & maintain Photography Strategy (physical and AI rendering)
• Work cross functionally with PDC & Warehouse leadership and UAW members
• Understanding of OE and Aftermarket content including ACES & PIES
• Collaborate and manage competing priorities with the Product Information Management (PIM) team, National Accounts, eCommerce, GM Parts Connect and other teams.
• Be able to lead and drive conversations
• Drive solutions to fill data gaps and perform data audits
• Ability to interpret data and tell a story about the data to leadership
• Produce visuals that help tell the story
• Develop and implement new processes, provide feedback, and work with others to ensure continuous improvement

Part Content & Analytics Lead

• Drive the design, delivery, and adoption of advanced analytics and machine learning solutions.
• Define and evolve the data science roadmap for GM Motorsports.
• Architect end-to-end ML and analytical solutions.
• Lead high-visibility projects from problem framing through delivery.
• Partner with race engineers and program managers to identify AI/ML opportunities.
• Provide technical mentorship and code/analysis review for junior and senior data scientists.
• Communicate complex analytical concepts to non-technical stakeholders.
• Champion data quality, governance, and responsible AI practices.

Staff Data Scientist – Motorsports

• Architect, develop, and deploy end-to-end agentic full-stack Generative AI (GenAI) applications that solve complex real-world problems, with a specific focus on improving engineering challenges and inefficiencies.
• Design and implement robust, scalable data science and Machine Learning Operations (MLOps) pipelines primarily within cloud environments like Google Cloud Platform (GCP) and Amazon Web Services (AWS), ensuring efficient deployment and maintenance of AI solutions.
• Lead the integration of new cloud technologies and AI tools (e.g., Vertex AI) into our workflows, continuously evaluating their potential and articulating their business value to drive innovation and efficiency.
• Develop intuitive and responsive front-end user interfaces that enable seamless interaction with agentic AI systems and effectively translate complex outputs into actionable insights.
• Acquire a deep understanding of vehicle engineering problems, translating them into appropriate mathematical representations and AI/ML solutions (classification, prediction, intelligent automation).
• Ensure the overall quality and integrity of data and solutions throughout the development lifecycle, from data collection and cleaning to model deployment.

See all jobs on JobTailor

Data Scientist – Supply Chain Analytics

Vehicle Connected Data Manager

Senior Data Scientist

Marketing Analytics Lead

Data & Analytics Lead

Marketing Analytics Lead

Data Scientist

Applied Data Scientist

Senior Data Scientist, Customer Care

Senior Revenue Cycle Analytics, Data Science Expert

People Analytics Leader, Journeys and COE Consulting

Data Science, AI Intern

Director of Data Science – Engineering

Data Scientist – Modeling and Analytics

Data Scientist

Head of Data and Machine Learning

Senior Data Scientist, Ecosystems

Part Content & Analytics Lead

Staff Data Scientist – Motorsports

Data Scientist