Data science, machine learning, optimization models, Master's degree in Machine Learning, Computer Science, Information Technology, Operations Research, Statistics, Applied Mathematics, Econometrics, Successful completion of one or more assessments in Python, Spark, Scala, or R, Using open source frameworks (for example, scikit learn, tensorflow, torch.

You have good knowledge and understanding of data science approaches including machine and deep learning; application in imaging domain (H&Es, IHC, radiomics) a plus. You will continue to stay up to date on the latest relevant scientific literature and translate your learnings into evolving best practices in data science at Genmab.

You will contribute to several clinical development programs to help identify appropriate biomarkers, mechanisms of action, and new indications using cutting-edge data science tools. You have a strong knowledge and practical experiences with high density data types in a clinical setting (genomics, transcriptomics, proteomics, flow cytometry, TCR sequencing, immune assays, etc.

Experience in Data science, machine learning, or optimization models Experience in Python, Spark, Scala, or R, using open source frameworks (for example: scikit learn, TensorFlow, Pytorch) You are knowledgeable of databases, data warehouse design, cloud storage, and ETL best practices.

For more than 20 years, its passionate, innovative and collaborative team has invented next-generation antibody technology platforms and leveraged translational research and data sciences, which has resulted in a proprietary pipeline including bispecific T-cell engagers, next-generation immune checkpoint modulators, effector function enhanced antibodies and antibody-drug conjugates.

Our core technical competencies include system-level modeling and gap analysis, advanced sensing and signal processing, machine learning and artificial intelligence, computational modeling, hardware and software prototyping, model-based systems engineering, establishing measures of performance, and human data collection in laboratory and field environments.

Understanding of Data Engineering, Data Science, Machine Learning, and AI workflows and their optimization. As we continue to invest in machine learning, data-driven modeling, CRM tools and innovation, the future is very exciting.

Explore new machine learning and artificial intelligence approaches for automated object detection, feature extraction/target state estimation, multi-source data fusion, and much more.

With minimal input from Senior Staff DS, adapt standard machine learning methods to solve business problems. You will work as part of a cross-functional agile team that includes data scientists, machine learning engineers, software engineers, designers, and product managers.

The search is for candidates at the interface of data science/machine learning and industrial engineering, operations research, and management science, including optimization, statistical learning, computational social science, healthcare, logistics, and operations.

Employ strong technical data science skills to build machine learning models to optimise digital customer journeys; pricing and promotions optimisation, recommenders and media campaign performance.

Interacting with internal customers to drive validation trials, implement process improvements, and integrate machine learning into current production.,QualificationsMinimum Qualifications:A MS or PhD Degree from an accredited university in Data Science, Computer Science, Computer Engineering, Mathematics, Statistics, Analytics, or related.

We're looking for a Machine Learning Engineer who will build out and design machine learning infrastructure for a team of data & remote sensing scientists, working collaboratively with the Analytics and Software teams to deliver data-driven products and solutions.

As a Machine Learning Engineer at Nelnet, you'll be at the intersection of data science and software engineering. Bachelor's or Master's in Computer Science, Machine Learning, Artificial Intelligence, or a related field.

Collaboration: Work closely with Data Engineers to ensure seamless data integration for machine learning models. Feature Engineering: Collaborate with Data Scientists to engineer features for machine learning models.