Data Scientist

• Lead the design, development, and implementation of advanced machine learning, generative AI solutions across structured and unstructured data
• Build and deploy scalable, automated data science products and reusable analytic assets that support business and client objectives
• Analyze large-scale, high-dimensional datasets using modern analytics tools and distributed computing platforms to identify trends, opportunities, and actionable insights
• Design and prototype agentic AI workflows leveraging large language models (LLMs), retrieval systems, structured data, APIs, tools, and business rules to automate complex business processes
• Translate business requirements into AI agent architectures, including task decomposition, tool orchestration, routing logic, escalation paths, and human approval checkpoints
• Optimize retrieval-augmented generation (RAG) solutions through embedding evaluation, metadata design, reranking approaches, citation quality assessment, and knowledge freshness validation
• Develop evaluation frameworks for machine learning and AI solutions, including performance, accuracy, reliability, hallucination risk, latency, cost, safety, and consistency measures
• Make key analytical and architectural decisions, establish technical standards, and provide leadership on methodology, model selection, solution design, and AI implementation best practices
• Lead code reviews, mentor data scientists, and promote best practices in software development, responsible AI, model governance, and analytics delivery
• Partner with engineering, platform, and business teams to deploy, monitor, and maintain production-ready analytics and AI solutions
• Translate complex analytical findings into clear reports, visualizations, and presentations for technical and non-technical audiences
• Collaborate with stakeholders and clients to define analytical approaches that address strategic, operational, and clinical objectives
• Evaluate emerging technologies and industry trends to advance organizational analytics and AI capabilities.

Lead Data Scientist

• Own deep learning and agentic AI initiatives end to end, from problem framing and data exploration through modeling, validation, deployment, and measurement.
• Partner directly with business and senior leaders to clarify objectives, constraints, and success criteria without relying on others to translate technical ideas. Proactively identify opportunities to apply data science to business challenges.
• Prepare and deliver executive-ready presentations that explain methodologies and recommendations, and present findings directly to stakeholders while answering questions in real time and defending technical decisions.
• Independently manage priorities, scope, timelines, risks, and stakeholder expectations across multiple concurrent efforts.
• Design, build, and evaluate deep learning models and agent based systems, selecting modeling approaches based on business needs, data constraints, and operational feasibility.
• Perform advanced data mining, simulation, feature engineering, and analysis on large and complex datasets.
• Translate model outputs into actionable, operational insights.
• Ensure data quality, reliability, and reproducibility; clearly communicate risks and limitations.
• Collaborate with engineering and platform teams to integrate models into production workflows.
• Produce clear, well-structured documentation covering problem definitions, methodologies, assumptions, results, and recommendations.
• Create artifacts (slide decks, summaries, dashboards, Confluence pages) that enable reuse without direct handholding.
• Establish and follow best practices for analytical rigor and reproducibility.

Senior AI Data Scientist

• Evaluate business objectives, collaborate in user research, understand process nuances, determine stakeholder needs, and identify requirements
• Champion the AI cause with stakeholder groups, educate and excite sponsors about possibilities offered by the data science discipline
• Choose best fit methods, define algorithms, validate, and deploy models to achieve business results
• Build AI solutions iteratively, in short cycles to enable a fail-fast, rapid learning environment
• Perform necessary data preparation and enhancements to models
• Interface directly with senior business leaders and stakeholders to architect and lead the development of advanced analytics solutions supporting Aircraft operations, Engineering, Supply Chain, Manufacturing, Quality, and Safety
• Work with data scientists leads across domains to ensure functional excellence in this technical field and will provide mentoring, technical leadership, and training to the data scientist community to accelerate and develop the talent base
• Provide thought leadership for machine learning and advanced analytics methods at Boeing and externally
• Engage with stakeholders across the business to identify applications across the enterprise, beyond the initial use case, and lead the implementation of these applications to capture business value
• Evaluate technologies, assist in identifying capability gaps, develop and teach best practices and technology standards, and work alongside the rest of the global data scientists and technical leads in Analytics organization
• Ensure analytics solutions meet the business requirements and are implemented in a timely manner to validate business value
• Work effectively with cross-functional teams in the analysis of highly complex data sets using advanced analytics techniques such as machine learning, advanced statistical analysis, visual analysis, text analysis, mathematical optimization, and simulation
• Identify modeling attributes and parameters
• Follow best practices and standard processes for model validation and refinement as per business requirements
• Assess performance of models and analysis and confirm that business objectives were met
• Work with business unit stakeholders to develop and sustain analytics solutions

Lead Artificial Intelligence/Machine Learning Data Scientist

• Lead brainstorming of new AI solutions
• Mentor data scientist community and provide technical leadership
• Develop and deploy AI and ML products
• Interface with senior leaders to architect advanced analytics solutions
• Work with cross-functional teams to analyze complex data sets
• Validate business value of analytics solutions

Lead Artificial Intelligence / Machine Learning Data Scientist

• Leads in developing, implementing, and managing organizational data solutions
• Establishes and maintains policies and procedures for high-quality data services
• Leverages technical knowledge to plan, design, build and maintain data solutions
• Develops strategies for data implementation, data acquisition, and archive recovery
• Advises and prepares information for senior leadership on critical organizational data
• Ensures integrity and maintenance of organizational data
• Resolves data integration and security issues for adherence to quality standards

Data Manager

• Solve business problems presented by stakeholders using data
• Pull, analyze, and interpret data relevant to scenarios
• Visualize data using Tableau
• Present findings to stakeholders
• Collaborate with data engineers

• Analyzing and interpreting large datasets to uncover potential revenue generation opportunities
• Collaborating with key stakeholders to comprehend business problems
• Utilizing data gathering and analysis techniques to devise solutions
• Presenting recommendations based on the findings
• Performing business analytics, which include data analysis, trend identification, and pattern recognition
• Applying agile practices for project management
• Creating and maintaining technical documentation
• Managing multiple priorities and maintaining quality and timeliness of work deliverables
• Delivering engaging presentations and effectively communicating technical concepts
• Mitigating risk by identifying potential issues and developing controls
• Researching the latest advances in the fields of data science and artificial intelligence

Data Scientist I

• Analyze large datasets to uncover trends, patterns, and insights that drive strategic business decisions.
• Develop and refine predictive models for customer segmentation, pricing optimization, and demand forecasting.
• Provide actionable data insights to support targeted marketing campaigns, customer retention strategies, and personalized communication.
• Partner with product, marketing, and customer experience teams to uncover opportunities for conversion and retention improvement.
• Apply advanced statistical methods and machine learning techniques to solve complex business problems and improve operational efficiency.
• Collaborate with data engineering to ensure data quality, consistency, and scalability across systems.
• Document methodologies and communicate findings clearly to both technical and non-technical stakeholders.
• Contribute to fostering a data-driven culture by sharing insights, tools, and best practices across teams.

Marketing Data Scientist

• Partner with a cross-functional team of data scientists, software engineers, and product managers to identify and quantify risks associated with models
• Leverage a broad stack of technologies — Python, Conda, AWS, H2O, Spark, and more — to reveal the insights hidden within huge volumes of numeric and textual data
• Lead teams of data scientists who build machine learning models to challenge current champion models
• Lead validation of a wide variety of models across multiple business domains

Principal Associate, Data Science – Model Risk Office

• Own and lead end-to-end execution of customer activation strategies, from requirements intake through deployment, QA, and post-launch validation.
• Design, build, and maintain end-to-end journey-based campaigns, including audience segmentation, triggers, decisioning, and orchestration.
• Translate marketing requirements into practical activation solutions within AEP and AJO
• Establish and enforce best practices for activation design, journey configuration, and execution standards.
• Partner with Analytics and business stakeholders to ensure campaigns deliver measurable outcomes
• Identify optimization opportunities to improve campaign performance, scalability, and operational efficiency.
• Provide day-to-day leadership, prioritization, and oversight for activation work across the team
• Support and maintain data pipelines and datasets within AEP/RTCDP to enable activation use cases
• Work with XDM schemas, audiences, and profiles to ensure data is structured and usable for segmentation and personalization
• Troubleshoot data quality, audience qualification, and activation issues
• Ensure adherence to data governance, consent, and privacy standards

Senior Marketing Activation, Data Lead

• 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

• Lead, coach, and grow a centralized team of data scientists working across pricing, forecasting, demand modeling, and broader predictive analytics—setting standards for technical rigor, code quality, and business impact.
• Own the modeling roadmap for the ML team, partnering with business and platform leaders to prioritize use cases, scope deliverables, and align modeling investments to measurable enterprise outcomes.
• Drive technical depth across the team—reviewing experiment design, feature engineering, model selection, validation strategy, and post-deployment monitoring with the rigor expected of a hands-on senior practitioner.
• Partner with AI Engineering, AI Product, and Data Engineering counterparts to ensure models are productionized on a modern MLOps stack with proper version control, retraining, and observability.
• Translate complex modeling work for executive audiences—framing tradeoffs, expected impact, confidence levels, and risks in language that supports clear decision-making.
• Build durable cross-functional partnerships with Pricing, Sales Operations, Supply Chain, Finance, and Corporate Analytics to ensure models are adopted, trusted, and tied to measurable business outcomes.
• Establish team operating cadence including planning, retros, model reviews, and documentation standards that scale as the team and portfolio of models grow.
• Recruit, develop, and retain top data science talent—owning hiring loops, leveling decisions, performance management, and career progression for direct reports.

Senior Manager, Data Science

• Leads the analysis of complex and unstructured data sets using advanced statistical methods for use in data driven decision making
• Responsible for leading cross-functional teams and providing in-depth data insights for complex business problems approached with advanced analytic techniques
• Manages engagement with internal customers on small and medium sized projects

• Build and deploy machine learning models to optimize revenue management and pricing strategies.
• Analyze large datasets to uncover insights and develop actionable recommendations for business decision-making.
• Collaborate with peers outside the data science team to align data science initiatives with business goals.
• Continuously evaluate and improve model performance through testing, tuning, and validation.
• Document processes, methodologies, and findings for both technical and non-technical audiences.

Senior Data Scientist

• Designing and implementing state of the art Large Language Model (LLM) based agents that seamlessly synthesize complex information and initiate important actions in a business workflow.
• Using advanced Generative AI techniques deriving actionable insights from unstructured text data, such as call transcripts and emails.
• Predicting client interest basis their digital footprint and making relevant recommendations to drive higher client value delivery
• Leverage statistical and machine learning techniques to extract actionable insights from client retention data.
• Develop customer churn prediction models that proactively identify at-risk clients.
• Build tools to process structured and unstructured data.
• Engineering features and signals to train ML model from diverse data collection

• responsible for the engineering work necessary for successful creation, deployment and managing of AI capabilities of the Intelligent Delivery Platform
• ensuring data quality
• creation of new data pipelines
• optimization and management of existing data pipelines
• ingestion and curation of data sources for Gen AI purposes (including chunking/embedding strategies for RAG system)
• AI Agent delivery
• Prompt Engineering
• selection and configuration of AI-specific tools and platforms
• management and monitoring of AI models through MLOps tools and model ops practices
• operationalizing AI capabilities, working closely with larger team who will supplement where traditional application development support is needed

Lead Data Science Engineer

• Use Python or R to explore data, perform analysis, and rapidly prototype analytical approaches within repeatable workflows.
• Design, develop, validate, and monitor interpretable machine learning models using sound statistical and modeling techniques.
• Own the end‑to‑end delivery of analytical solutions—from prototype through testing, validation, and scalable production deployment—collaborating closely with engineering and testing partners to ensure production readiness.
• Act as a key communication bridge across business, product, and engineering teams to gather and document requirements, clearly communicate analytical solutions, and ensure business needs are accurately implemented.
• Define and track performance metrics to measure solution effectiveness and business impact.

Data Scientist – Corporate & Institutional Banking

• Performs analytical tasks on structured and unstructured data to extract actionable business insights.
• Participates in the data gathering, data processing and data mining of large and complex datasets.
• Develops algorithms using advanced mathematical and statistical techniques like machine learning to predict business outcomes and recommend optimal actions to management.
• Runs analytical experiments in a methodical manner to find opportunities for product and process optimization.
• Assists in the presentation of business insights to management using visualization technologies and data storytelling.
• May partner with Data Architects, Data Analysts, Data Engineers and Visualization Experts to develop data-driven solutions for the business.

Data Scientist, Gen AI Governance

• Provides financial and regulatory reporting and analyses to maintain adequate controls over the financial and regulatory reporting processes
• Responsible for running complex business performance, risk and operational analytics
• Develops analytical methods/models to assess market, credit and/or operational risk of new and existing financial products
• Analyzes large datasets to improve risk adjusted returns, deliver profitable growth, and communicate conclusions
• Synthesizes analytical results to develop strategies that improve lending decisions and manage risk
• Applies predictive models and other tools to develop and execute appropriate segmentation and targeting for acquisition
• Works with business, credit, data, and model development partners to design and monitor test designs and analytical reporting

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