Bioinformatics Data Analyst PG

Bayer•Chesterfield, MO

About The Position

At Bayer we’re visionaries, driven to solve the world’s toughest challenges and striving for a world where 'Health for all Hunger for none’ is no longer a dream, but a real possibility. We’re doing it with energy, curiosity and sheer dedication, always learning from unique perspectives of those around us, expanding our thinking, growing our capabilities and redefining ‘impossible’. There are so many reasons to join us. If you’re hungry to build a varied and meaningful career in a community of brilliant and diverse minds to make a real difference, there’s only one choice. Bioinformatics Data Analyst PG Position Overview: In this role, you will maximize the use of large-scale genomics and metadata to support high throughput DNA genotyping and sequencing production and workflow development needs. You will independently perform bioinformatic analysis, computer programming, and experimental design. Your expertise will be crucial in using and maintaining advanced bioinformatic pipelines and databases for mining and visualizing large genomic data sets to deliver insights, recommendations, and solutions. You will provide technical contributions in a fast-paced team environment to accelerate our efforts on building an analytics-driven product pipeline. Additionally, you will develop sustainable, consumable, accurate, and impactful reporting on analysis inputs, analysis outputs, biological interpretation, business impact, and key performance indicators. Presenting compelling, validated stories to all levels of the organization, including peers, senior management, and internal customers, will be key to driving both strategic and operational changes in the business. You will demonstrate a moderate level of autonomy in developing relationships for effective cross-functional collaboration throughout multiple organizations and partners on work stream initiatives for the Data Science Community.

Requirements

Minimum of a Bachelor's degree with 5 years of experience
Educational preparation or applied experience in at least one of the following areas: 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 bioinformatic analyses and pipelines using shell scripting, R, Python, or other statistical and/or mathematical programming packages
Demonstrates knowledge in Plant Breeding, Genetics, and/or Genomics
Some familiarity with cloud deployment pipelines and/or high-performance computing
Experience with Next Gen sequencing analysis and sequencing lab processes
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 bioinformatic analyses in a clear, concise, and actionable manner to extended team and small groups of key stakeholders

Nice To Haves

Master's degree with 2 years of experience, or a PhD

Responsibilities

Maximize the use of large-scale genomics and metadata to support high throughput DNA genotyping and sequencing production and workflow development needs
Independently perform bioinformatic analysis, computer programming, and experimental design
Use and maintain advanced bioinformatic pipelines and databases for mining and visualizing large genomic data sets to deliver insights, recommendations, and solutions
Provide technical contributions in a fast-paced team environment to accelerate our efforts on building an analytics-driven product pipeline
Develop sustainable, consumable, accurate, and impactful reporting on analysis inputs, analysis outputs, biological interpretation, business impact, and key performance indicators
Present compelling, validated stories to all levels of the organization, including peers, senior management, and internal customers to drive both strategic and operational changes in business
Demonstrate a moderate level of autonomy in developing relationships for effective cross-functional collaboration throughout multiple organizations and partners on work stream initiatives for the Data Science Community