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

• Design and develop Reinforcement Learning models to optimize collections strategies, customer treatment paths, and recovery outcomes.
• Build adaptive decisioning systems using techniques such as:
• - Q-Learning
• - Deep Q Networks (DQN)
• - Policy Gradient Methods
• - Contextual Bandits
• - Markov Decision Processes (MDP)
• Develop sequential and behavioral models for customer engagement, repayment prediction, and collections prioritization.
• Apply stochastic modeling and probabilistic methods to optimize dynamic treatment strategies under uncertainty.
• Collaborate with business stakeholders to translate collections and risk management problems into scalable AI/ML solutions.
• Build and maintain machine learning pipelines in Databricks or similar distributed computing environments.
• Conduct experimentation, simulation, and offline policy evaluation to validate RL strategies before deployment.
• Work with large-scale structured and unstructured datasets to derive actionable insights and improve operational performance.
• Partner with engineering and MLOps teams to deploy and monitor production-grade ML/RL models.
• Mentor junior data scientists and promote best practices in modeling, experimentation, and AI governance.

Senior Data Scientist – Reinforcement Learning

• Design and develop Reinforcement Learning models to optimize collections strategies, customer treatment paths, and recovery outcomes.
• Build adaptive decisioning systems using techniques such as:
• Q-Learning
• Deep Q Networks (DQN)
• Policy Gradient Methods
• Contextual Bandits
• Markov Decision Processes (MDP)
• Develop sequential and behavioral models for customer engagement, repayment prediction, and collections prioritization.
• Apply stochastic modeling and probabilistic methods to optimize dynamic treatment strategies under uncertainty.
• Collaborate with business stakeholders to translate collections and risk management problems into scalable AI/ML solutions.
• Build and maintain machine learning pipelines in Databricks or similar distributed computing environments.
• Conduct experimentation, simulation, and offline policy evaluation to validate RL strategies before deployment.

Data Scientist – Reinforcement Learning

• 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

• 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 research, design, and implementation of advanced Machine Learning, Deep Learning, and Time Series Forecasting models to solve complex enterprise business and planning challenges.
• Architect GenAI solutions, focusing on fine-tuning proprietary and open-source Large Language Models (LLMs) via Transformer architectures for specialized enterprise data tasks.
• Design and integrate Conversational AI and autonomous Agentic AI workflows to create intuitive experiences that can independently execute complex planning tasks.
• Collaborate with Engineering, Product, and Design teams to transition AI models from early prototypes into robust, highly scalable production systems.
• Serve as a core subject matter expert, mentoring cross-functional teams and driving a culture of technical excellence, rigorous testing, and continuous learning.

Principal AI Data Scientist

Principal Data Scientist – AI

• Lead the end‑to‑end enterprise portfolio operations function.
• Establish and maintain portfolio governance frameworks, scorecards, and KPIs.
• Champion enterprise data transformation strategy.
• Analyze and optimize cross‑functional processes, workflows, and operating models.
• Serve as a trusted advisor to senior leadership.
• Assess organizational structure, roles, and capabilities; recommend improvements.

Head of Data Transformation – Portfolio Operations

• 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

• Scoping and designing NLP/NLU applications that support clinical understanding of medical documents and downstream healthcare workflows.
• Translating business and clinical requirements into mathematical and experimental criteria, ensuring models are measurable, reliable, and auditable.
• Processing, aligning, and enriching diverse structured and unstructured medical datasets for deep learning, LLMs, and agentic AI systems.
• Developing and evaluating deep learning models, including transformer-based architectures and neural architectures such as CNNs and attention mechanisms for capturing linguistic patterns in clinical text.
• Understanding and mitigating model weaknesses, including bias, drift, hallucination behavior, and robustness concerns.
• Designing experiments and error analyses that explain model behavior and enable clear communication with both technical and non-technical audiences.
• Deploying models into Solventum’s cloud environment, partnering with ML engineering to ensure reliability, observability, and compliance within regulated healthcare systems.
• Staying current with emerging research in NLP/LLMs, evaluating new methods and identifying opportunities to advance Solventum’s deep learning initiatives.

• This Data Science Manager Senior role focuses on delivering decision ready insight through Descriptive (“what happened?”) and Diagnostic (“why did it happen?”) Analytics, with concrete outputs such as executive dashboards, KPI frameworks, trend and cohort analyses, drill downs, segmentation, driver analysis, and root cause investigations.
• The role is accountable for translating complex data into clear performance narratives by defining standardized metrics, ensuring data quality and lineage, and embedding analyses into scalable BI assets rather than ad hoc reporting.
• As needed, the role performs Prescriptive Analytics (“what should we do?”) by turning findings into decision recommendations through scenario analysis, sensitivity testing, and practical optimization or decision models, and by operationalizing insights into dashboards, alerts, and playbooks.
• Predictive Analytics (“what will happen?”) is in scope but not required, with forecasting, risk or propensity scoring, and applied machine learning viewed as nice to have capabilities.
• The manager leads and prioritizes an analytics roadmap, coaches analysts and data scientists, and promotes disciplined use of tools such as SQL, Python, modern BI platforms, cloud data technologies, Git, and applied statistics.
• Strong partnership with product, operations, risk, and business leaders is essential to frame the right questions and deliver executive ready storytelling that connects analysis to action.
• The role enforces data governance, documentation, and audit ready practices, ensuring metric consistency and trusted reporting across teams.
• An experiment and measurement mindset is expected, applying test and learn or A/B testing to evaluate impact. Any models or advanced techniques used are monitored for performance, reliability, and fairness, with assumptions and limitations communicated transparently.

Senior Data Science Manager – Data, Modeling & Analytics

• Provide senior analytical leadership to optimize the bank’s centralized marketing decisioning engine and drive customer and business outcomes.
• Focus on improving the quality, performance, and stability of self learning models by developing analyses and business cases while partnering closely with data science teams and key business stakeholders.
• Shape and govern decision inputs, including algorithm refinement, value frameworks, propensity models, constraints, and business rules, while incorporating enterprise and channel context and ensuring regulatory compliance.
• Serve as mentor to more junior employees.
• Consult with clients and mentor consultants on tool and strategy implementation and monitoring, statistical scoring, business intelligence, data quality, and analytical product / solution development.
• Determine the optimal analytic approach and support development, implementation and enhancements.
• Analyze results and make recommendations for key business partners and senior management or communicate conclusions from complex analytical solutions to a wide range of audiences.

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