Description

Job Title: Associate Data Scientist - Computational Biology
Work Location : HyderabadCorteva Agriscience is a publicly traded, global pure-play agriculture company that provides farmers around the world with the most complete portfolio in the industry - including a balanced and diverse mix of seed and crop protection solutions focused on maximizing productivity to enhance yield and profitability. Our mission is to enrich the lives of those who produce and those who consume to ensure progress for generations to come.
We are seeking a \'Associate Data Scientist - Computational Biology\' at our Corteva Corporate Office, Madhapur, Hyderabad, INDIA. We leverage data science and multi-omics data to discover, optimize and develop novel traits, chemistry products and biologicals. As an Associate Data Scientist, you will be part of a diverse team of Data Scientists and Analysts. A critical aspect of this position involves working with global teams across multiple locations. Communication and teamwork are key elements of this role; excellent verbal and written communication skills are required. Position Responsibilities

• Design and execute genomics experiments, analyses, design pipelines and workflows to solve complex biological problems.
• Integrate and analyze multiple datatypes in the context of experimental observations with a goal towards formulating testable hypothesis.
• Applying innovative computational methods and influence adoption of omics strategies for multiscale genomics and molecular analytics in plant systems.
• Collaborate with multi-disciplinary teams for experimental design, genomic data generation and analyses to influence the discovery by identifying novel patterns.
• Have expertise in predictive algorithms and methods for understanding genes, promoters, and other regulatory elements in complex eukaryotic genomes.
• Ability to design and analyze large scale data for specialized genomics technologies such as CAGEseq, single cell seq, ATACseq, Drop-seq, etc
• Communicate the results, interpretations and conclusions of bioinformatics analysis projects including the confidence of predictions and suggest relevant/ potential experiments for confirming computational analysis-based predictions.
• Follow emerging trends in genomics data analysis and maintain expertise in the application of genomic technologies.
• Collaborate effectively in a team environment with individuals of all levels of experience.

Qualifications

Education and Experience

• PhD in Computational Biology, Bioinformatics, Computer Science or Statistics with 2-4 years of work experience in academic or corporate setting.
• Experience in Python and R programming languages is required.
• Experience in NGS datasets and different areas of genome sequence analysis including comparative genomics, genome assembly, alignment, gene structure and gene function annotation methods and tools.
• Good working experience in high performance computing (distributed computing, workflow management, version control) is required. Working knowledge of AWS and Kubernetes environments will be a plus.
• Demonstrated experience with popular NGS analysis software's including BWA, bowtie, GATK-suite, HiSat, Spades, Trinity, RSEM, HTSEq, sequence databases, etc.
• Experience in development of bioinformatics workflows or large-scale computational biology pipelines and databases demonstrated through publications or similar.
• Demonstrated experience of applying AI/ ML on molecular data will be preferred.
• Strong critical thinking and problem-solving skills, flexibility, and willingness to learn.
• Strong organizational, interpersonal and communication skills (oral and written) are a must.
• We are looking for an individual who will enhance foundational knowledge of genomes for crops and other species of interest to further our understanding of the biological mechanisms underpinning various crop traits. This person will exhibit deep competence in developing high-quality, comprehensive genome structural and functional annotations, interpreting large scale genomics and transcriptomics datasets, collaborating extensively, and who can stretch their imagination to solve biological problems of business interest with scientists from other domains.