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

• You are the ideal candidate if you have experience managing large scale, complex programs and projects related to business process and system transformation, ideally ERP implementation using SAP’s S4 HANA solution.
• The Functional Data Lead Analyst will coordinate conversion activities, translate and document conversion requirements and detailed plans.
• They will drive execution tasks with various team members and stakeholders, track progress, and escalate issues.
• The Functional Data Lead Analyst will be expected to follow project management processes and best practices to ensure all tasks, activities, and resources are aligned to meet the overall program and organizational goals.
• Work cross-functionally to complete the build phase, supporting system configuration design, testing, migration and hyper-care activities
• Complete conversion functional specification documents, value mapping, cross reference documents and data model documents across multiple deployments
• Drive conversion progress, including task completion and milestone achievement to adhere to overall timelines
• Lead meetings with cross functional topic leads
• Understand team member workload, potential resource constraints, and ensure optimal resource utilization throughout the conversion process
• Proactive risk management: identify, assess, mitigate, and monitor potential risks with real-time alerts for critical issues
• Monitor and track conversion and deviation issue resolution
• Communicate expectations and instill accountability in team members
• Resolve conflicts, promote work sharing, and motivate teams toward common goals
• Manage multiple conversion activities, tasks and resources through effective organization prioritization and time management practices to meet program objectives
• Additional responsibilities include documentation, profiling analytics, reporting, internal stakeholder communication, and identifying areas for improvement to enhance quality and efficiency of cutover activities

SAP S/4 HANA Data Lead Analyst

• Analyzing and interpreting large datasets to uncover potential revenue generation opportunities and develop effective risk management strategies
• Collaborating with key stakeholders to comprehend business problems
• Utilizing data gathering and analysis techniques to devise solutions
• Presenting recommendations based on the findings
• Developing advanced technical documentation for an array of models
• Working closely with developers to understand how the model works and ensure results are accurate
• Partnering with technology and model users to schedule deployments
• Conducting regular model monitoring and sharing performance results

Data Scientist I – Fraud Model Governance

• Analyze and model operational, economic, financial, and organizational data
• Help quantify competitive performance
• Evaluate the impact of operational changes
• Develop data-driven strategies and solutions to complex business problems
• Build modeling frameworks and predictive models for new datasets using advanced analytics
• Explore data to test hypotheses, identify important causes, and answer business questions
• Develop and run statistical programs/tests to analyze complex relationships
• Incorporate industry/competitor insights, stay current on best practices, and propose innovative solutions
• Research and maintain awareness of insurance industry best practices and our strategies

Senior Actuarial Data Scientist – Hybrid

• 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, focusing on extracting structured data from technical drawings, purchasing orders, and other complex documents.
• Provide technical leadership to the Generative AI team, setting technical direction, defining best practices, and ensuring the team follows industry standards in AI and ML development.
• 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

• 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.

• 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

• 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

• Design, build, and deploy autonomous AI agents using frameworks like Amazon Bedrock and AgentCore to solve business problems in pricing, sales, operations, and customer interactions.
• Apply machine vision and feature extraction on home attributes (photos, plans, finishes) to inform premium pricing and personalization strategies.
• Engineer and maintain data pipelines and systems supporting all models and agents, ensuring scalability and reliability.
• Integrate agents with enterprise systems and protocols (MCP servers, A2A protocol, internal APIs).
• Design and run experiments (A/B tests, multi-armed bandits, uplift models) to measure and optimize model and agent performance.
• Ensure observability and reliability of deployed agents, including logging, evaluation, monitoring, and drift detection.
• Proactively gather feedback from stakeholders and adapt solutions for adoption and measurable impact.
• Translate complex data science and statistical concepts into clear recommendations, stories, and visualizations for executives and non-technical audiences.
• Favor incremental, explainable solutions that deliver quick wins and scale over time.
• Drive experimentation with new tools and approaches, ensuring robustness, governance, and scalability in production deployments.
• Share learnings with the broader team to raise the bar on data science and agentic development across the organization.
• Manage timelines and expectations transparently with both the data science team and business stakeholders.

Lead Data Scientist

• Develop and execute a comprehensive data strategy for TD, focusing on advanced automation, data integration, and FAIR data practices; in close alignment with TD and IT.
• Lead the design and implementation of scalable data pipelines to support discovery workflows.
• Partner with TD leaders to build strong external partnerships with industry consortia and academic partners pertaining to Data Science needs for TD.
• Co-Champion data governance, analytics, model lifecycle management (MLOps), and Responsible AI standards into reusable capabilities that can be shared elsewhere in the organization.
• Lead a core team of data scientists and engineers to support TD in reaching its strategic goals.
• Collaborate with TD teams (such as: In Silico Discovery and Discovery Technologies & Molecular Pharmacology), IT, R&D Data Science, and external partners to jointly introduce emerging technologies such as generative and agentic AI, multimodal analytics, and advanced automation tools that benefit TD’s business objectives.
• Collaborate with TD teams (such as: In Silico Discovery and Discovery Technologies & Molecular Pharmacology), IT, R&D Data Science, and external partners to jointly introduce data and technology innovation standards.
• Works with peers across Discovery, Product Development, & Supply (DPDS) and our Therapeutic Areas to generate and analyze our data in the best way possible for opportunities in Therapeutic Discovery (for example: experiment design, molecule design, lab process automation, etc.)

Senior Director, Head of Data Science – Digital Health, Therapeutics Discovery

• We are seeking a visionary Director, R&D Neuroscience Data, Data Science & Artificial Intelligence – Ophthalmology to join the Neuroscience Data, Data Science & Artificial Intelligence (DDSAI) team.
• This leader will shape and execute innovative strategies leveraging multimodal data sources, digital health technologies, computer vision, artificial intelligence (AI), and clinical/real-world evidence (RWE) to accelerate drug discovery and development, and maximize patient impact.
• By combining ophthalmology expertise with strong data science acumen, this role will enhance clinical trial execution and ensure that new solutions are patient-centric and ready for regulatory and payer acceptance.
• As an integral member of a highly matrixed team, the Director will collaborate with cross-functional experts in the Neuroscience Therapeutic Area, Clinical Development, Quantitative Sciences, Regulatory Affairs, and Patient-Reported Outcomes, and forge strategic external partnerships to infuse new ideas and capabilities.
• This is a unique opportunity to redefine how we understand and treat eye diseases—uncovering novel digital biomarkers and endpoints, stratifying patients for more personalized care, and ultimately delivering better outcomes for people living with ophthalmic diseases.

Director, R&D Neuroscience Data, Data Science & Artificial Intelligence

• Collaborate in the development and application of advanced AI/ML methods, including cutting-edge computer vision techniques applied to ophthalmic imaging data (e.g., Optical Coherence Tomography and fundus images), to uncover disease mechanisms and identify novel biomarkers.
• Collaborate in the development and validation of novel digital endpoints.
• Engage with regulatory stakeholders to ensure these innovations enhance clinical trial design, improve patient monitoring and care pathways, and meet regulatory requirements.
• Develop and apply sophisticated statistical models using real-world and clinical data to generate insights into disease progression, treatment outcomes, and patient stratification.
• Leverage longitudinal disease modeling, Bayesian methodologies, and causal inference techniques to inform decision-making.
• Apply emerging generative AI approaches to boost data analysis and knowledge discovery, integrating diverse multimodal datasets (imaging, clinical, wearable, etc.) for a more holistic understanding of ophthalmic diseases.
• Partner with Clinical Development and Medical Affairs to integrate RWE into evidence generation strategies.
• Support trial optimization and regulatory submissions by incorporating insights from large-scale clinical datasets, electronic health records (EHRs), and other real-world data sources.
• Build strong cross-functional collaborations within the company and spearhead external partnerships with academic institutions, technology providers, regulators, and industry consortia.

Associate Director, R&D Neuroscience Data, Data Science & AI – Ophthalmology

• Drive enterprise aligned AI governance across Data Science & Digital Health (DSDH) and the broader R&D organization
• Implement high quality, compliant, and scalable governance practices for both traditional AI/ML and GenAI systems
• Partner closely with DSDH teams, Quality, Legal, Global Regulatory Affairs, and Johnson & Johnson Technology to streamline governance processes
• Embed AI safety protocols and align with enterprise strategic priorities
• Develop and maintain an AI governance roadmap that supports R&D strategy
• Develop, implement, and drive adoption of quality assurance processes for AI/ML and GenAI development
• Integrate those processes with existing medical product and digital health tool development processes
• Create and disseminate playbooks, toolkits, and standardized guidance to support teams in implementing responsible AI practices
• Establish and track metrics to evaluate governance effectiveness including processes for audits, reviews, and risk based assessments of AI systems
• Serve as a technical and strategic advisor on AI governance topics
• Represent Johnson & Johnson in external AI governance forums and consortia

Associate Director – AI Quality and Governance, R&D Data Science & Digital Health

• Drive the development and application of advanced AI/ML methods, including cutting-edge computer vision techniques applied to ophthalmic imaging data (e.g., Optical Coherence Tomography and fundus images).
• Lead the development and validation of novel digital endpoints.
• Engage with regulatory stakeholders to ensure these innovations enhance clinical trial design, improve patient monitoring and care pathways, and meet regulatory requirements.
• Develop and apply sophisticated statistical models using real-world and clinical data to generate insights into disease progression, treatment outcomes, and patient stratification.
• Leverage longitudinal disease modeling, Bayesian methodologies, and causal inference techniques to inform decision-making.
• Apply emerging generative AI approaches to boost data analysis and knowledge discovery, integrating diverse multimodal datasets (imaging, clinical, wearable, etc.) for a more holistic understanding of ophthalmic diseases.
• Partner with Clinical Development and Medical Affairs to integrate RWE into evidence generation strategies.
• Build strong cross-functional collaborations within the company and spearhead external partnerships with academic institutions, technology providers, regulators, and industry consortia.

Director, R&D Neuroscience Data, Data Science & Artificial Intelligence – Ophthalmology

• Develop and maintain an AI governance roadmap that supports R&D strategy, enabling informed decision making and proactive management of AI related risks.
• Develop, implement, and drive adoption of quality assurance processes for AI/ML and GenAI development, integrate those processes with existing medical product and digital health tool development processes, and ensure adherence to internal and external regulatory standards.
• Drive alignment on AI quality/governance initiatives across R&D and enterprise, partnering with JJT to streamline governance workflows and promote consistent adoption across R&D.
• Create and disseminate playbooks, toolkits, and standardized guidance to support teams in implementing responsible AI practices.
• Establish and track metrics to evaluate governance effectiveness, including processes for audits, reviews, and risk based assessments of AI systems.
• Serve as a technical and strategic advisor on AI governance topics
• Collaborate with therapeutic areas, functional partners, and senior stakeholders to drive cross functional collaboration and alignment.
• Represent Johnson & Johnson in external AI governance forums and consortia, contributing to industry wide standards and best practices.

Associate Director, AI Quality and Governance – R&D Data Science, Digital Health

• Develop, implement, and deploy machine learning models and statistical analyses to optimize energy storage and solar asset performance across Stem's portfolio
• Design and build data pipelines to extract, transform, and analyze large-scale datasets from energy systems
• Collaborate with software engineering, product management, and energy experts to integrate data-driven solutions into PowerTrack platforms
• Create data visualizations, dashboards, and reports to communicate insights and recommendations to stakeholders
• Conduct exploratory data analysis to identify trends, patterns, and opportunities for improving system performance and business outcomes

See all jobs on JobTailor

Data Scientist

Senior Data Scientist

Data Science Manager

SAP S/4 HANA Data Lead Analyst

Data Scientist I – Fraud Model Governance

Senior Actuarial Data Scientist – Hybrid

Principal Data Scientist – Gen AI

Principal Data Scientist – Gen AI

Ads AI Analytics Lead

Principal Data Scientist, Health Informatics

Senior Data Scientist – Level I

Lead Data Scientist

Senior Director, Head of Data Science – Digital Health, Therapeutics Discovery

Senior Director, Head of Data Science – Digital Health, Therapeutics Discovery

Director, R&D Neuroscience Data, Data Science & Artificial Intelligence

Associate Director, R&D Neuroscience Data, Data Science & AI – Ophthalmology

Associate Director – AI Quality and Governance, R&D Data Science & Digital Health

Director, R&D Neuroscience Data, Data Science & Artificial Intelligence – Ophthalmology

Associate Director, AI Quality and Governance – R&D Data Science, Digital Health

Data Scientist, Forecasting – Energy Storage System Optimization