Data Scientist

• Design and deploy machine learning, NLP, and generative AI solutions that help researchers discover, understand, and apply scientific knowledge.
• Build intelligent retrieval, search, recommendation, ranking, and question-answering systems that improve research outcomes.
• Develop AI systems that connect information across publications, datasets, citations, knowledge graphs, and scientific ontologies.
• Fine-tune, evaluate, and integrate large language models and retrieval-augmented generation (RAG) systems into production environments.
• Create robust evaluation frameworks that measure quality, reliability, relevance, trustworthiness, and user impact.
• Build scalable data pipelines and machine learning workflows that support experimentation, monitoring, and continuous improvement.
• Apply the appropriate combination of classical machine learning, deep learning, retrieval, and generative AI techniques to solve complex scientific problems.
• Collaborate with engineering, product, UX, analytics, and domain experts to transform ambiguous challenges into practical solutions.
• Contribute clean, maintainable, production-quality Python code and reusable AI components.
• Continuously improve the capabilities, performance, and real-world value of AI systems that support scientific discovery.

• Design, develop, and deploy advanced machine learning, NLP, retrieval, and generative AI solutions that support scientific discovery and knowledge exploration
• Build and optimize LLM-powered applications, including question answering, literature summarization, semantic search, research insight generation, and evidence-grounded AI experiences
• Develop retrieval-augmented generation (RAG) systems that connect AI models with trusted scientific and scholarly content
• Create intelligent capabilities for search, ranking, recommendation, entity extraction, classification, enrichment, and decision support
• Design evaluation frameworks that measure quality, relevance, reliability, grounding, trustworthiness, and user impact
• Integrate knowledge graphs, ontologies, taxonomies, citations, metadata, and scientific domain knowledge into AI workflows
• Partner with engineering teams to produce, monitor, optimize, and continuously improve AI systems at scale
• Lead technical discovery, influence solution architecture, and guide methodological decisions across initiatives
• Mentor fellow data scientists and contribute to a culture of technical excellence, experimentation, and responsible AI
• Collaborate closely with Product, Engineering, Research, Editorial, UX, and domain experts to solve complex scientific and business challenges

Senior Data Scientist

• Lead and develop high-performing teams of data scientists, machine learning engineers, researchers, and technical contributors.
• Define and execute data science strategies that advance scientific discovery, research intelligence, and knowledge-access products.
• Drive the development of AI-powered capabilities across search, retrieval, recommendation, NLP, knowledge systems, and generative AI.
• Translate complex customer, scientific, and business challenges into scalable data science solutions and measurable outcomes.
• Establish high standards for experimentation, evaluation, model quality, reliability, and responsible AI practices.
• Partner closely with Product, Engineering, Research, UX, Analytics, and domain experts to shape product strategy and delivery.
• Mentor and coach team members while fostering a culture of scientific rigor, collaboration, innovation, and continuous learning.
• Guide the adoption of emerging AI technologies, including LLMs, retrieval-augmented generation, semantic search, and knowledge-based systems.
• Influence senior stakeholders and contribute to long-term AI, technology, and product strategy across the organization.
• Ensure that AI systems are trustworthy, scalable, explainable, measurable, and aligned with meaningful customer and societal outcomes.

Data Science Manager

• Own and drive an 18-24 month technical roadmap that balances innovation with disciplined business value delivery.
• Develop and deploy generative AI models and large language models (LLMs) for multimodal document processing.
• Provide technical leadership to the Generative AI team, setting technical direction and defining best practices.
• Influence the roadmaps, priorities, and resourcing of partner engineering, product, and business teams.
• Serve as the technical architect for the embedded DFM AI + IQE track, leading the integration of Xometry’s proprietary AI directly into the Siemens ecosystem.
• Design and implement efficient workflows for data preparation, cleaning, and augmentation to support the training of generative AI models.
• Serve as a respected subject matter expert and mentor to scientists and engineers across the organization.

Principal Data Scientist – Gen AI

• Develop and maintain Python-based data pipelines that collect, process, and ingest data from multiple sources.
• Improve existing data workflows with a focus on quality assurance, sustainability, documentation, and maintainability.
• Ensure analytic workflows are reproducible, well-structured, and scalable.
• Support responsible handling of sensitive data in compliance with FERPA and AACOM data governance standards.
• Develop dashboards and data visualization tools primarily using Tableau and Python.
• Maintain and enhance existing dashboards, internal analytics tools, and client-facing data products.
• Translate complex datasets into accessible and actionable visual insights.
• Translate defined research designs into executable analytic workflows.
• Clean, transform, and validate complex administrative and survey datasets.
• Conduct quantitative analyses with increasing independence over time.
• Contribute to applied research initiatives including workforce studies, survey analysis, and text-based or NLP projects.
• Support the development and operationalization of statistical and/or ML models used in research and operational initiatives.
• Partner with colleagues across AACOM departments to support strategic data and analytics initiatives.
• Contribute to a culture of innovation, continuous learning, and process improvement.
• Document workflows, methodologies, and technical processes clearly and consistently.
• Stay current on emerging tools, technologies, and best practices in data science and analytics.

• 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

• Lead the design and implementation of next-gen media measurement and optimization capabilities, integrating marketing mix, long term brand equity measurement, in-flight measurement (e.g., lift studies, A/B testing), pre-market factors that affect media (e.g., creative testing)
• Develop media-driven volume forecasting tools to integrate into McCormick’s demand forecasting platform
• Drive and leverage external connections to find new and improved tools and approaches to bring into McCormick
• Partner with vendors used for outsourced models, serving as lead consultant on technical methodology alignment integration with new capabilities
• Develop training content that enables business partners to use marketing optimization capabilities more effectively

Global Data Scientist – Marketing Measurement and Optimization

• 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

• Provide advanced analytics expertise within the Business Analytics function
• Independently apply data science techniques to solve business problems
• Develop, validate, and maintain predictive models and AI solutions
• Utilize multiple sources of data and various machine learning techniques
• Write clean, modular, and well-documented code for production
• Interpret data and model outputs to generate actionable insights

Senior Data Scientist – Level I

• Analyze data to support or disprove a thesis
• Select and implement the right tools for the job
• Build, train, test, and validate models
• Engineer models into production
• Document your work clearly
• Monitor and improve models in production
• Explore agentic and reasoning systems
• Perform other duties as assigned

Data Scientist II

• Use analytical skills and data science knowledge to create real-world impact
• Work closely with clients to understand their questions and needs
• Dig into their data-rich environments to find pieces of their information puzzle
• Develop algorithms and systems
• Use the right combination of tools and frameworks to turn sets of disparate data points into objective answers
• Provide a deep understanding of the data, meaning, and how it can be used

• Lead the development, validation, and deployment of predictive models, including time series forecasting, anomaly detection, and simulation models, to address USCIS mission and operational challenges.
• Prepare, transform, and analyze large and complex datasets using SQL and Python in a Databricks environment.
• Perform exploratory data analysis, feature engineering, and model optimization to ensure high-quality, repeatable, and interpretable results.
• Design and deliver advanced dashboards and visualizations to communicate insights, forecasts, and scenarios to technical and non-technical stakeholders.
• Apply advanced statistical and machine learning techniques to identify patterns, measure operational performance, and anticipate future trends.
• Partner with program managers, analysts, and client stakeholders to frame research questions, design analytic studies, and present findings in a clear, actionable manner.
• Document methodologies, ensure reproducibility of analyses, and contribute to analytic best practices across the team.
• Mentor junior analysts and data scientists in best practices for data science, forecasting, and statistical modeling.

• 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

• Rigorously investigating, evaluating, interpreting, and delivering data-driven findings from relevant and diverse data sources.
• Preparing and delivering timely and actionable tactical analysis products tailored to the needs of the customer mission.
• Transforming and analyzing raw data into actionable findings, and communicating through charts, maps, timelines, and infographics to visualize complex relationships and big data sets in a user-friendly and easy-to-understand format to support operational decision making or criminal prosecution.
• Learning, applying, and maintaining relevant subject matter expertise in one or more technical areas in support of customer missions, through analytic work, operational tactics, data handling, methodology development, command briefings, and training and consultation.
• Rapidly responding to changing circumstances requiring flexibility, time-management, and the ability to function under shifting priorities while keeping track of ongoing requirements and deadlines.
• Communicating and collaborating on analysis with both internal and external analytic colleagues and with operational customers, in person or remotely, and interacting effectively with command-level and senior leadership.

• Setting the technical direction and measurement strategy for the shopping funnel
• Developing and mentoring data scientists across experimentation, causal inference, and product analytics
• Defining scalable frameworks for A/B testing, funnel optimization, and consumer behavior modeling
• Influencing senior product and business stakeholders with data-driven recommendations
• Ensuring analytical rigor translates into tangible product improvements and business growth
• Driving deep-dive analyses of renter behavior, market dynamics, and funnel performance to identify high-impact growth opportunities
• Managing and developing a team of data scientists working across shopping funnel measurement and consumer analytics
• Raising the bar on production-grade analytics and experimentation
• Operating as a strategic thought partner to senior stakeholders across Product, Engineering, and Business Strategy
• Leading thoughtful adoption of AI within your team.

Manager, Data Science

• Unlock the secrets held by data sets
• Turn complex data into useful information to solve global challenges
• Use analytical skills and data science knowledge to create real-world impact
• Work closely with clients to understand their questions and needs
• Develop algorithms and systems to turn disparate data points into objective answers
• Provide a deep understanding of the data and how it can be used

• Provide technical literacy on Department of War technology areas to support the Join Acquisition Protection and Exploitation Cell (JAPEC)
• Identify and mitigate threats to critical and emerging technologies
• Inform operations of the DoW and its partners using advanced data science techniques
• Query and collate unclassified information sources, identify trends, and provide data visualization through dashboards
• Collaborate across multiple interagency teams, law enforcement, and counterintelligence entities
• Draft comprehensive analytical documents for executive risk assessment decisions

Data Scientist III

• Actionable Insight: Generate compelling and actionable insights from complex, multi-source marketing data sets that directly inform channel investment, campaign design, and pipeline strategy.
• Stakeholder engagement: Establish strong collaborative relationships with marketing leaders and operators across demand gen, partner, field, product marketing, and brand, delivering high-impact analytics initiatives that translate loose, evolving requirements into clear deliverables.
• Attribution & Measurement: Design, build, and maintain the attribution framework for UpGuard – spanning first and multi-touch attribution and incrementality testing – and clearly communicate the trade-offs and assumptions behind each lens.
• Channel & Program Analytics: Develop a deep, first-principles understanding of channel logic across paid media, organic, content, lifecycle, partner, and field, and build the metrics, models, and dashboards that let each program owner self-serve their performance.
• Data Products: Partner with the data engineering team to design, construct, and maintain foundational marketing data assets – translating loose marketing requirements into well-specified dbt models that the whole organisation can rely on.
• Business Intelligence: Partner strategically with marketing stakeholders to provide robust self-service and conversational analytics capabilities, using design thinking principles to build user-friendly dashboards for funnel performance, channel ROI, partner sourced pipeline, and field event attribution.
• Deep Dive analysis: Personally conduct thorough, hands-on, technical analysis to diagnose and solve the most significant marketing challenges – from channel saturation and diminishing returns to lead quality decay and campaign cannibalisation.
• Commercial Analysis: Provide ongoing operational support to the commercial growth of the organisation, connecting marketing investment to pipeline, ARR, and payback in ways that finance and the executive team trust.

Marketing Analytics Lead

• Work from Anywhere: Senior Revenue Cycle Analytics & Data Science Expert (Remote).
• Join a team in a fully remote capacity.
• Focus on leveraging data science—including predictive and prescriptive analytics.
• Tackle high-value operational challenges such as denials management, AR optimization, and cash forecasting.
• Develop, test, and deploy standard reports and certified datasets.

Senior Revenue Cycle Analytics, Data Science Expert

• 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.

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