Precision Genomics Data Science Co-Op

At Bayer, we are shaping the future of agriculture for farmers, consumers, and the planet. We are seeking a talented Data Scientist co-op that will help fundamentally transform genotyping high throughput operations and genomics data quality and delivery in the Precision Genomics by functioning as a scientific leader in predictive modeling.

YOUR TASKS AND RESPONSIBILITIES

The primary responsibilities of this role are to: 

This individual inside the Precision Genomics group works collaboratively with interdisciplinary scientists, IT, and engineering professionals across the organization to optimize DNA sequencing workflows and answer important questions that drive key decisions for Precision Genomics and our partners including Breeding, Biotech and Product Supply. ​

• Works in the Precision Genomics Analytics team as an individual contributor with other Data Analysts/Data Scientists;
• Providing technical contributions in a fast-paced team environment to accelerate our efforts on building an analytics-driven product pipeline;
• Using advanced mathematical models, machine learning algorithms, and/or operations research techniques, and strong business acumen to deliver insight, recommendations, and solutions;
• Developing sustainable, consumable, accurate, and impactful reporting on model inputs, model outputs, observed outputs, business impact, and key performance indicators;
• Forming partnerships with cross functional teams in the company, including internal R&D, engineering and IT teams;
• 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.

WHO YOU ARE

 Bayer seeks an incumbent who possesses the following:

Required Qualifications:

• Currently pursuing a Masters or PhD in a relevant discipline (Data Science, Machine Learning, Electrical/Industrial Engineering, Statistical Genetics, Statistics, Biostatistics, Bioinformatics, Genomics, Computational Biology, Applied Mathematics, Computer Science 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;
• Intermediate proficiency in machine learning algorithms and concepts;
• 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.

Preferred Qualifications:

• 1-2 years of experience with machine learning modeling and working with genomics data;
• Strong analytical and problem-solving skills.