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Post Doctoral Researcher – AI/ML, Optimization
Johnson & Johnson. Apply machine learning, generative AI, and optimization techniques to develop computational solutions supporting global clinical operations .
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in machine learning, generative AI, and optimization techniques to develop computational solutions for clinical operations. Proficient in building predictive models, conducting stochastic simulations, and communicating complex findings to diverse stakeholders.
Highest-signal resume keywords
Ph.D. In Computer Science Or Related FieldMulti-Modal ML Predictive ModelingGenAI And Clinical LLM ExperiencePython And SQL ProficiencyMulti-Objective Optimization Engines
ATS Keywords
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Hard Skills
Machine LearningGenerative AIOptimization TechniquesStochastic SimulationsPredictive ModelingData AnalysisDocument ExtractionClinical Concept InterpretationTime-Series ForecastingData Quality Monitoring
Soft Skills
Scientific CommunicationCollaborationAnalytical ThinkingProblem SolvingStakeholder Engagement
Tools & Technologies
Python LLM ToolsDSPyLangChainScikit-learnXGBoostOptunaPyMCMLflowKedroGit
Industry Keywords
Clinical OperationsReal World DataElectronic Health RecordsClinical Trial DataData HarmonizationPrivacy PracticesData Quality PracticesHealth EconomicsBiostatisticsEpidemiology
Tech Stack
Tools & technologiesJenkinsPythonScikit-LearnSQL
About the role
Key responsibilities & impact- Apply machine learning, generative AI, and optimization techniques to develop computational solutions supporting global clinical operations
- Conduct research and build, test, and improve ML, GenAI, and optimization pipelines for clinical development and trial operations
- Analyze operational, real-world, and cost data to build ML models and optimization engines
- Predict outcomes, highlight tradeoffs between competing trial objectives, recommend optimal operational scenarios, and generate actionable insights
- Develop and evaluate LLM-enabled workflows for document extraction, protocol comparison, trial similarity assessment, data harmonization, clinical-concept interpretation, and eligibility-criteria assessment
- Use stochastic simulations to forecast operational and patient-journey outcomes, including enrollment and patient study completion
- Design experiments, evaluation metrics, and frameworks for validation, monitoring, data quality, and responsible AI
- Contribute reusable code, data pipelines, and documentation with data scientists, engineers, and clinical-development partners
- Communicate research findings, analytical methods, and recommendations to technical and non-technical stakeholders
- Contribute to internal or external scientific dissemination
Requirements
What you’ll need- Ph.D. degree, completed or expected before the start date, in computer science, statistics, biostatistics, biomedical informatics, epidemiology, health economics, applied mathematics, engineering, or another quantitative discipline
- Hands-on experience with multi-modal ML predictive modeling and stochastic simulations for time-series forecasting
- Experience with GenAI and clinical LLMs for document parsing, clinical concept disambiguation, or harmonization
- Experience building multi-objective optimization engines using evolutionary algorithms, reinforcement learning, or mixed-integer linear programming
- Proficiency with Python and SQL
- Experience with Python LLM tools such as DSPy and LangChain, optimization tools such as pymoo, and ML tools such as Scikit-learn, XGBoost, Optuna, and PyMC
- Familiarity with clinical operational data, real world data, electronic health records, claims, and financial data
- Record of scientific communication through publications, conference presentations, open-source contributions, or equivalent research outputs
- Preferred: Experience with EHR, claims, registry, CTMS, EDC, or clinical-trial data and related privacy and data-quality practices
- Preferred: Experience in MLOps practices and tools including MLflow and Kedro; Git usage; CI/CD stacks including Jenkins and GitLab; DevOps tools
Benefits
Comp & perks- Inclusive work environment
- Equal opportunity employment
- Interview process accommodations for applicants with disabilities