Senior Director Applied Machine Learning

Key Skills

Java Programming, Python Programming, SQL, Algorithms, Econometrics, Data Architecture

Job Description

The primary responsibilities of this role, Senior Director Applied Machine Learning, are to:

• Set direction and create roadmap of ML applications across Research and Development (R&D) that will deliver business in close partnership with RDLT members;
• Ensure the delivery and execution of roadmap;
• Be responsible for developing and executing machine learning applications across R&D that will drive drug discovery and development to a new generation of targets with significantly increased probability of success and enhancing productivity;
• Work collaboratively with internal stakeholders, and external partners to drive prioritization of opportunities across the R&D (portfolio and asset level) and lead applied machine learning capabilities for business impact;
• Partner with scientific experts and R&D leadership to develop and implement world class ML approaches to provide a competitive advantage in disease understanding, biomarker development, target validation, speed to develop medicines and probability of success in humans;
• Identify and address most complex problems from drug discovery (OMICS, compound screening, safety prediction, target validation, structural biology etc.) through to development and the commercialization of new medicines;
• Manage a team of machine learning engineers, foster collaboration, encourage their career growth, and invest in skills development;
• Increase the machine learning acumen of the broader R&D community;
• Develop and maintain best practices for machine learning applications in partnership with data science and machine learning research teams;
• Ensure organization is using cutting edge algorithms especially emerging approaches, skills and technologies, focusing them on digital delivery innovation;
• Design and implement scalable, efficient, automated processes for model development, model validation, model implementation and large-scale data analysis including providing thought leadership to the broader R&D.

Domestic relocation assitance is available. May consider other Bayer locations.

WHO YOU ARE

Your success will be driven by your demonstration of our LIFE values. More specifically related to this position, Bayer seeks an incumbent who possesses the following:

Required Qualifications:

• Ph.D. Degree in Computer Science, Machine Learning, Data Science, Bio-Medical or an equivalent highly technical field;
• With at least 14 years of related experience in fields such as artificial intelligence, machine learning, with a focus in life sciences industry preferred. Experience in the pharma/biotech R&D from target discovery through clinical development;
• At least six years of experience leading teams that have developed and deployed AI/ML models with material impact leveraging deep learning algorithms and model architectures, knowledge of the most advanced and up-to-date ML platforms and frameworks;
• Digital literacy and proven ability to drive change using novel technologies;
• Deep professional and technical expertise in designing, implementing, testing, and validating state-of-the-art scientific products leveraging broader AI/ML techniques, preferably with exposure to the biopharma industry;
• Strong external network in innovative digital technologies;
• Team player with the ability to drive cross-disciplinary collaborations while demonstrating humility and resiliency;
• Cultural agility and experience working within a diverse and complex organizations
• Hands on experience developing, scaling and implementing machine learning using relevant programming languages (such as Python), state of the art deep learning frameworks and big data tools;
• Experience with advanced ML techniques, Multi-task Learning, Transfer Learning, Reinforcement Learning as well as Unsupervised and Semi-supervised Learning;
• Experience with designing and deploying products for users across R&D value chain that leverage machine learning at scale;
• Track record of publications and contributions to the machine learning community at large;
• Excellent problem-solving skills and ability to dive into learning algorithms, evaluation metrics, model architecture, code, test plans, project plans, deployments and operations;
• Comfort communicating and interacting with scientists, engineers, and product managers as well as understanding and translating the science of AI and Machine Learning to a more general audience.