Data Engineer (m/f), La Dargoire

Bayer is an international group whose core businesses lie in the healthcare and agriculture sectors. At the intersection of chemistry and biology, Bayer helps to improve quality of life by offering products and services in human and plant health.

Missions

• The Computational Life Science Disease Control (CLS-DC) group acts to enable dataand model informed innovation and decision making within the SMol group in general & the Disease Control indication in particular to improve the success rate in the discovery and optimization process of novel fungicidal compounds with high commercial potential.
• At equal importance the team will ensure the drive of digital maturity in the SMol organization with a special focus on Disease Control indication.
• Communicate learnings and success with global reach. Help building a trustful, sustainable and impactful collaboration between CLS & Small Molecules Disease Control.
• Regularly deliver digital value add to project decisions and process debottlenecking. Realize the impact by validate insights on an integrative data driven understanding.

Key Tasks

• Contribute to SMol CLS strategy for data & could enablement and ensure alignment with IT.
• Enable data science with SMol by Integrating, transforming, validating and reconciling large amounts of data.
• Assemble and evaluate data such that new insights, solutions, and visualizations can be derived.
• Provide technical contributions of the CLS data science team to accelerate our efforts on building an analytics-driven product pipeline by using global platforms (prems & cloud) & resources wherever possible.
• Exploratory data analysis, data cleaning & processing Perform data discoveries to understand data formats, source systems, file sizes, etc. and engage with business partners in this discovery process.
• Provide support to our data scientists in optimizing Python and/or R code, move analysis pipelines to containers etc.
• Provide support & guidance to our data scientists for coding standards and methodologies for specific domain specific programming languages like Python and R.
• Identify trends / data quality issues / anomalies across large data sets, particularly in the area of machine data, and other agronomic management practices.
• Evaluate the limitations of existing data sources and provide recommendations to address unmet data needs.
• Write robust and well-documented research code
• Undertake written & verbal communication with stakeholders in various parts of the organization
• Ensure Deliver results in a timely and efficient manner.

Key Working Relation

• Actively contribute to the development of the CLS strategy in SMol by being at the interface with groups within SMol (CLS & Indications/Technologies functions), RegScience, Field Solution & IT organization in a complex multidisciplinary and multicultural environment.
• Building relationships and networks within current function and cross-functionally, interacts most often with Data Scientists and Engineers across multiple organizations and key stakeholders within the business including senior level management
• Provides support for their team without management presence and is expected to provide technical leadership to all levels of the organization
• Be a cooperative member of the CLS-DC strategy by ensuring that projects fit in the global CLS-DC approach.

Scope: Global

Qualifications

• Master level, with relevant experience in computational sciences, preferably computational biology or an equivalent qualification
• 5+ years of relevant development experience with recent experience in Python/R & SQL/SPARQL
• Experience working with cloud tools such as AWS and Domino data science platform
• Understanding of LifeScience R&D and background/experience in Biology, Biochemistry or chemistry
• Strong organizational skills, interpersonal skills, written and oral communications
• Strong problem solving and proven project delivery skills
• Demonstrated experience with global multi-disciplinary teams & learning the science
• Enthusiast, creative, proactive, bold and out-of-box thinking
• High energy, clear goal orientation and strong work ethic; and a “can do” attitude
• Willingness and curiosity to learn experimental set-ups and challenges
• Excellent verbal and written communication skills and proficient in English