Whippany , NJ
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Data Scientist #61292

Whippany, NJ Full-time
Posted on October 6, 2019

Data Scientist Bayer U.S. LLC's Whippany, NJ, office seeks a Data Scientist to enable scientists to identify data-driven answers by building diagnostic and predictive models using first and third party data.

The primary responsibilities of this role, Data Scientist, are to: 

  • leading design, development, and maintenance of Artificial Intelligence systems; 
  • Applying feature engineering and iterative analytics on data to extract valuable insights from clinical and electronic health data; 
  • advising scientists in regards to patterns and relationships in data to recommend business direction and outcomes; 
  • researching and developing advanced machine learning algorithms which provide insights into data beyond human judgment;
  • translating requirements from business into advanced analytics models.

Your success will be driven by your demonstration of our Life values. More specifically related to this position, Bayer seeks an incumbent who possesses the following:

  • Master’s degree (or foreign equivalent degree) in Information Technology, Data Analytics Engineering, Data Science, Machine Learning, or a directly related field;
  • three (3) years of experience in a related position;
  • scripting languages such as R, Python, and SQL 
  • transforming data into insights with visualization tools such as Tableau and Ggplot/Matplotlib 
  • working with databases, e.g. SQL Server, Oracle and structured data from multiple CSVs;  
  • working in a cloud environment (e.g. AWS, Azure); 
  • building predictive models, regression analysis, A/B Testing, statistical modeling and data simulation, time-series forecasting, NLP and text analysis, classification algorithms, supervised and unsupervised machine learning models (e.g., SVM, Random Forest, XGBoost, Neural Networks, and KNN & K-means clustering); 
  • research and development in data science and machine learning problems; 
  • awareness of relevant statistical measures and machine learning performance metrics, e.g. AUC/ROC; 
  • Bayesian statistics; 
  • anomaly detection algorithms, e.g. Auto-encoders, DB-Scan; 
  • working with/in a Unix/Linux environment and Git version control; and (xi) Agile and Scrum methodologies.

Experience can be concurrent.