Biometrics/biostatistics, Computer systems analysis/Programming
Corteva Agriscience™ has an exciting opportunity for a Data Scientist to join our Data Science group located in Indianapolis, IN. We are seeking a strong Data Scientist with background in Statistics and Machine Learning to support and drive our growing data science efforts in Bioengineering and Bioprocessing R&D. The candidate will be joining a strong, globally distributed data science team that develops and applies innovative tools and techniques for analyzing datasets towards delivering insights for our R&D and other functions.
The candidate must have a strong technical background and demonstrated expertise in applying state-of-the-art data analytics for research problems using diverse datasets. Preference will be given to candidates with familiarity with biotechnology.
Key responsibilities include:
Promoting the application and adoption of statistical analysis, machine learning modeling and data science capabilities for Bioengineering and Bioprocessing R&D through strong technical and interpersonal abilities
Partnering with leading R&D scientists to advance discovery, characterization, development, and manufacture of natural products through data science
Providing statistical and machine learning expertise, and collaborate with scientists to improve hypothesis formulation, experimental design, data collection, modeling, process design and interpretation of complex datasets to enable data-driven decisions
Developing and deploying end-to-end data engineering and data science pipelines at scale for users with diverse backgrounds in chemical and biological disciplines
Ph.D. degree in Statistics, Biostatistics, Computer Science (Machine Learning) or related fields. Two or more years experience post PhD is preferred but not required.
Extensive experience with big data engineering, descriptive statistics, dimensionality reduction, predictive modelling and validation (mixed-models, non-linear regression, principal components, cross-validation techniques, etc.)
Knowledge of simulation techniques and optimization methods with multiple constraints