Computational Biologist

Overview

URUS is seeking a Computational Biologist to join our Innovation group as part of the team focused on Genetic Advancement and Trait Discovery. This role is suited for a creative and career-motivated individual who brings strong expertise across multiple disciplines of genomics, bioinformatics, and systems biology. The successful candidate will lead the integration, analyses and interpretation of genomic and multi-omics data to identify biological pathways, DNA sequence motifs or genomic variation for the rationale design of genetic traits to express phenotypes that significantly improve fertility, protein quality, health or well-being across cattle breeding systems.

• The main objectives of our Innovation team are to better understand genomics and genetics at the molecular level and apply these findings to accelerate genetic improvement and reproductive efficiencies of select proprietary lineages of dairy and beef cattle.
• The Computational Biologists  goals are to deliver tools and knowledge that facilitate commercial implementation for advancing these goals in the URUS breeding program. The role requires analyzing large biological datasets, modeling complex systems, and developing predictive tools, alongside possessing creative analytical and problem-solving abilities and interdisciplinary communication skills to bridge biology and computer science for breakthroughs of economic importance to cattle genetics.

• Ph.D. in Computational Biology, Systems Biology, Genome Science, or a related field.
• Biology:
  Deep understanding of genetics, molecular biology, biochemistry, cellular functions, and organismal systems.
• Mathematics & Statistics:
  Expertise in probability, statistical modeling, and algorithm design.
• Computer Science:
  Principles of programming, data structures, algorithms, high-performance computing (HPC), and database management.
• Bioinformatics:
  Knowledge of specific tools, databases, and analysis methods for genomic/proteomic data.
• Livestock Genetics and Genomics:
  Knowledge and experience in working with genomic and genetic data from food animals is preferred.
• Programming:
  Proficiency in languages like Python (with libraries like Pandas, Num Py, scikit-learn) and R. Experience managing bioinformatics pipelines in Unix/Linux environments.
• Data Analysis & Machine Learning:
  Strong skills in data mining, machine learning (deep learning, pattern recognition), and biostatistics. Proficient in genome-wide association studies (GWAS) and fine-mapping methods to identify causal variants and regulatory regions. Proven experience in identifying causal variants is preferred.
• Data Handling:
  Ability to manage, integrate, analyze, and visualize large, complex datasets (e.g., Next-Generation Sequencing data and other Omics datasets). Knowledge of Snakemake, Microsoft Azure and Azure Databricks is preferred.
• Modeling & Simulation:
  Experience with mathematical modeling of biological processes and molecular dynamics.
• Problem-Solving:
  Excellent analytical thinking to translate biological questions into computational problems (e.g., demonstrated skill to integrate multi-omics datasets to identify target genes or regulatory motifs/mechanisms within the expressed genome that link genotype to phenotype).
• Communication &
  
  Collaboration:
  
  Fluency in English. Ability to explain complex technical findings to both technical and non-technical audiences. Works within interdisciplinary teams (e.g., molecular and quantitative geneticists, data scientists, and reproductive biologists). The incumbent is responsible for analyzing, interpreting, and reporting research data. Results, in the form of manuscripts, reports, and presentations at scientific meetings, are considered authoritative and technically accurate.
• Research Acumen:
  Skill in formulating hypotheses, finding relevant data, and designing computational experiments.
• Adaptability:
  Eagerness to learn new methods and tools to tackle evolving biological challenges.
• Attention to Detail:
  Precision in coding, data interpretation, and validation.
• Strategic Planning for Innovation:
  Incumbent is responsible for planning specific research approaches and designing experimental schemes to achieve research objectives. Contribute to long-term research planning and innovation strategies, which include active participation in team and cross-functional meetings.
• Originality and creativity are required to integrate appropriate company resources, methodologies, technologies into a cohesive set of analyses that successfully characterize genome structure and function. Also required to integrate complex datasets from separate functional and structural genome studies into a federated database that channels subsequent studies towards elucidating how alterations in complex biological processes affect phenotypic expression.

As a holding company with cooperative and private ownership, URUS is a family of businesses at the heart of the dairy and beef industry – Alta Genetics, GENEX,…