Data Scientist Computational Genetics

Data Scientist Computational Genetics

YOUR TASKS AND RESPONSIBILITIES

The primary responsibilities of this role Data Scientist Computational Genetics are to: 

• Provide technical contributions in a fast paced team environment to accelerate our efforts on building an analytics driven product pipeline using genotypic and genomic data;
• Independently perform statistical analysis computer programming predictive modeling and experimental design;
• Build cross functional relationships to collaboratively partner with the business and effectively network within the Data Science Community;
• Use advanced mathematical models machine learning algorithms operations research techniques and strong business acumen to deliver insight recommendations and solutions;
• Contribute to the development of code bioinformatic analyses pipelines computational tools and databases for mining and visualizing large genomic data sets;
• Develop sustainable consumable accurate and impactful reporting on model inputs model outputs observed outputs business impact and key performance indicators;
• Present compelling validated stories to all levels of organization including peers senior management and internal customers to drive both strategic and operational changes in business.

Domestic relocation as well as visa sponsorship is available 

WHO YOU ARE                                         

Your success will be driven by your demonstration of our LIFE (Leadership Integrity Flexibility Efficiency) values. More specifically related to this position Bayer seeks an incumbent who possesses the following:

Required Qualifications:

• Bachelor’s degree with five years of experience or Master’s degree with two years of experience or PhD;
• Educational preparation or applied experience in at least one of the following areas: Computational Biology Statistical Genetics Machine Learning Biostatistics Bioinformatics Genomics Applied Mathematics Computer Science Operation Research or other related quantitative discipline;
• Demonstrates intermediate proficiency in computational skills and level of experience building data models using R Python or other statistical and/or mathematical programming packages;
• Strong proficiency in predictive modeling to include comprehension of theory modeling/identification strategies and limitations and pitfalls;
• Experience in successful delivery of valuable analysis through application of domain knowledge; evidence of ability to strong business acumen;
• Strong communication competencies to include presentations and delivery of complex quantitative analyses in a clear concise and actionable manner to extended team and small groups of key stakeholders.