Principal Data Scientist - Ai/Ml And Optimization

Experteer Overview As Principal Data Scientist in Ju**amp;J Innovative Medicine, you will lead the design and deployment of ML, optimization, and GenAI solutions to optimize global clinical operations.You will work within the DDSAI GD team to translate data into actionable strategies for enrollment forecasting, cost estimation, and site selection.You will integrate predictive models with optimization engines to support data?driven decision making and proactive planning.This role offers impact across diverse therapeutic areas and a mission to advance health outcomes through cutting?edge analytics.Compensaciones / Beneficios * Conceive, develop, and implement ML, multi-objective optimization, GenAI solutions for clinical trial operations * Build predictive models using real-world and cost data to support operational planning * Formulate and solve optimization problems balancing cost, timelines, patient burden, quality, and efficiency * Integrate predictive modeling with optimization to evaluate scenarios and recommend strategies * Create explainable decision-support systems for proactive planning and risk identification * Adapt large language models for information extraction and analytics on trial protocols and eligibility * Run stochastic enrollment simulations to forecast outcomes like enrollment and study completion * Communicate complex methods/results clearly to diverse audiences to inform decisions * Coach and train junior colleagues in techniques and responsibilities Responsabilidades * Ph.D. in a quantitative discipline * 5+ years of industry experience in data science projects using ML, optimization, NLP, and GenAI * Hands-on with multi-modal ML, time-series forecasting, and stochastic simulations * Experience building multi-objective optimization engines (evolutionary algorithms, RL, MILP) * GenAI and clinical LLMs for document parsing and concept harmonization * Proficiency in MLOps (MLflow, Kedro) and CI/CD tools (Jenkins, GitLab) * Proficiency in Python and SQL * Experience with Python LLM tools (DSPy, LangChain), optimization tools (pymoo), ML tools (Scikit-learn, XGBoost, Optuna, PyMc) * Experience with clinical operational data, RWD, EHR/claims, and financial data Requisitos principales * annual bonus potential * vacation days * parenteral leave for minimum 12 weeks * bereavement and caregiver leave * well-being reimbursement * insurance plans availability

Compensaciones / Beneficios * Conceive, develop, and implement ML, multi-objective optimization, GenAI solutions for clinical trial operations * Build predictive models using real-world and cost data to support operational planning * Formulate and solve optimization problems balancing cost, timelines, patient burden, quality, and efficiency * Integrate predictive modeling with optimization to evaluate scenarios and recommend strategies * Create explainable decision-support systems for proactive planning and risk identification * Adapt large language models for information extraction and analytics on trial protocols and eligibility * Run stochastic enrollment simulations to forecast outcomes like enrollment and study completion * Communicate complex methods/results clearly to diverse audiences to inform decisions * Coach and train junior colleagues in techniques and responsibilities Responsabilidades * Ph.D. in a quantitative discipline * 5+ years of industry experience in data science projects using ML, optimization, NLP, and GenAI * Hands-on with multi-modal ML, time-series forecasting, and stochastic simulations * Experience building multi-objective optimization engines (evolutionary algorithms, RL, MILP) * GenAI and clinical LLMs for document parsing and concept harmonization * Proficiency in MLOps (MLflow, Kedro) and CI/CD tools (Jenkins, GitLab) * Proficiency in Python and SQL * Experience with Python LLM tools (DSPy, LangChain), optimization tools (pymoo), ML tools (Scikit-learn, XGBoost, Optuna, PyMc) * Experience with clinical operational data, RWD, EHR/claims, and financial data Requisitos principales * annual bonus potential * vacation days * parenteral leave for minimum 12 weeks * bereavement and caregiver leave * well-being reimbursement * insurance plans availability