Principal Data Scientist - Ai/Ml And Optimization

Experteer Overview As Principal Data Scientist - AI/ML and Optimization at Ju**amp; J Innovative Medicine, you will design and deploy ML, optimization, and GenAI solutions to improve global clinical operations.You will work within DDSAI GD to support enrollment forecasting, cost estimation, and country/site selection, aligning with the broader mission to advance healthcare.You’ll build predictive models and optimization engines, integrate them for scenario planning, and translate insights into actionable plans.This role offers impact across clinical trials and operations, with opportunities to mentor colleagues and shape data-driven decisions.Compensaciones / Beneficios - Conceive, develop, and implement ML, multi-objective optimization, GenAI solutions for clinical trial operations - Leverage real-world data, cost data, and operational data to build predictive models and optimization engines for data-driven planning - Develop time-series forecasting models for operational KPIs and outcomes - Solve optimization problems to balance cost, timelines, patient burden, quality, and efficiency - Combine predictive modeling with optimization to evaluate scenarios and advise on strategies - Build explainable decision-support systems translating analytics into actionable insights - Adapt large language models for information extraction and analytics on trial protocols and eligibility criteria - Run stochastic enrollment simulations to forecast patient journeys and study completion - Articulate technical methods clearly to diverse audiences and train junior colleaguesResponsabilidades - Ph.D. in a quantitative discipline - 5+ years of industry experience delivering data science projects using ML, optimization, NLP, and GenAI - Hands-on experience with multi-modal ML and time-series forecasting - Experience building multi-objective optimization engines using evolutionary algorithms, RL, or MILP - Experience with GenAI and clinical LLMs for document parsing and data harmonization - Proficiency in MLOps (MLflow, Kedro); Git; CI/CD tools (Jenkins, GitLab) - Proficiency with Python and SQL - Experience with Python LLM tools (DSPy, LangChain), optimization tools (pymoo), and ML tools (Scikit-learn, XGBoost, Optuna, PyMC) - Familiarity with clinical operational data, RWD, EHR/claims, and financial dataRequisitos principales - annual bonus - vacation days - parental leave (12 weeks) - bereavement leave - caregiver leave - volunteer leave

This role offers impact across clinical trials and operations, with opportunities to mentor colleagues and shape data-driven decisions.Compensaciones / Beneficios - Conceive, develop, and implement ML, multi-objective optimization, GenAI solutions for clinical trial operations - Leverage real-world data, cost data, and operational data to build predictive models and optimization engines for data-driven planning - Develop time-series forecasting models for operational KPIs and outcomes - Solve optimization problems to balance cost, timelines, patient burden, quality, and efficiency - Combine predictive modeling with optimization to evaluate scenarios and advise on strategies - Build explainable decision-support systems translating analytics into actionable insights - Adapt large language models for information extraction and analytics on trial protocols and eligibility criteria - Run stochastic enrollment simulations to forecast patient journeys and study completion - Articulate technical methods clearly to diverse audiences and train junior colleaguesResponsabilidades - Ph.D. in a quantitative discipline - 5+ years of industry experience delivering data science projects using ML, optimization, NLP, and GenAI - Hands-on experience with multi-modal ML and time-series forecasting - Experience building multi-objective optimization engines using evolutionary algorithms, RL, or MILP - Experience with GenAI and clinical LLMs for document parsing and data harmonization - Proficiency in MLOps (MLflow, Kedro); Git; CI/CD tools (Jenkins, GitLab) - Proficiency with Python and SQL - Experience with Python LLM tools (DSPy, LangChain), optimization tools (pymoo), and ML tools (Scikit-learn, XGBoost, Optuna, PyMC) - Familiarity with clinical operational data, RWD, EHR/claims, and financial dataRequisitos principales - annual bonus - vacation days - parental leave (12 weeks) - bereavement leave - caregiver leave - volunteer leave