This stuff is happening right now. The melding of supercomputers and health sciences gives us the digital biology we've been babbling about.
From Sanofi:
date posted 02/21/2024contract type Full timejob id R2713383location Cambridge, MassachusettsReference No. R2713383
Position Title:Computational Scientist – (Machine Learning) Digital R&D Large Molecule Research Team
Department:Data Strategy Program Management
Location: Cambridge, MA
About Sanofi:
We are an innovative global healthcare company, driven by one purpose: chasing the miracles of science to improve people’s lives. Our team, across some 100 countries, is dedicated to transforming the practice of medicine by working to turn the impossible into the possible. We provide potentially life-changing treatment options and life-saving vaccine protection to millions of people globally, while putting sustainability and social responsibility at the center of our ambitions.
Sanofi has recently embarked on a vast and ambitious digital transformation program. A cornerstone of this roadmap is the acceleration of its data transformation and of the adoption of artificial intelligence (AI) and machine learning (ML) solutions to accelerate R&D, manufacturing and commercial performance, and bring better drugs and vaccines to patients faster, to improve health and save lives.
In alignment to our digital transformation, we have launched a new major strategic initiative in 2023: the Biologics x AI Transformation. This is positioned to be a unique data-driven team, with expertise in AI platforms, data engineering, ML operations, data science, computational biology, strategy, and beyond. We are working as one to identify, design, and scale state-of-the-art AI capabilities targeted to truly transform how we research biologics.
Who You Are:
You are a dynamic Computational Scientist who will work with other scientists to apply cutting-edge computation, Machine Learning/Deep Learning approaches to revolutionize our large molecule computational tools by contributing to accelerating and improving the process of design and engineering of novel biologics drug candidates.
Job Highlights:
Apply and develop artificial intelligence and machine learning (AI/ML) approaches (e.g. classification, clustering, machine learning, deep learning) on pharma research data sets (e.g. activity, function, ADME properties, physico-chemical properties, etc.)
Building models from internal and external data sources, algorithms, simulations, and performance evaluation by writing code and using state-of-the art machine learning technologies.
Close interactions with other Computational scientists, data engineers, software engineers, UX designers, as well as research scientists in core scientific platforms focusing on protein therapeutics, in an international context (US, Europe, China)
Update and report relevant results to interdisciplinary project teams and stakeholders
Key Functional Requirements & Qualifications:
Advanced degree (e.g. M.Sc., PhD) in a field related to AI/ML or Data Analytics such as: Computer Science, Mathematics, Statistics, Physics, Biophysics, Computational Biology or Engineering Sciences.
Ideally 1+ years of industry experience, new grads will also be considered. Should have a track record of applying ML/Deep Learning (DL) approaches to solve molecule-related problems.
Familiarity with protein structure or sequence featurization/embeddings.
Familiarity with advanced statistics, ML/DL techniques including various network architectures (CNNs, GANs, RNNs, Auto-Encoders, Transformers, PLM etc.), regularization, embeddings, loss-functions, optimization strategies, or reinforcement learning techniques.
Proficiency in Python and deep learning libraries such as PyTorch, TensorFlow, Keras, Scikit-learn, Numpy, Matpilotlib.
Familiarity with data visualization and dimensionality reduction algorithms
Ability to develop, benchmark and apply predictive algorithms to generate hypotheses
Comfortable working in cloud and high-performance computational environments (e.g. AWS)
Excellent written and verbal communication, strong tropism for teamwork
Strong understanding of pharma R&D process is a plus.
