Essential Functions and Duties- Design and implement complex data engineering processes to support innovative data science modeling
- Collaborate with chemistry and biology research teams to design data pipelines, analyze experimental data and implement experimentally actionable feed-back loops
- Apply and deploy established and novel statistical and machine learning algorithms to explore, understand and optimize properties of the vast delivery vehicle space, both in silico and experimentally
- Develop robust, scalable workflows and maintain security controls to protect sensitive data across cloud and on-premise environments
- Coordinate with cross-functional teams to deploy models and communicate results and with a focus on computational efficiency, performance, and usability
- Design of repositories, CI/CD pipelines and integration tests for ML workflows
QualificationsMS in Computer Science, Data Science, Statistics, Computational Biology, Computational Chemistry, or a related discipline with 2 years hands-on machine learning experience.
Knowledge, Skills, and Abilities - Track record developing statistical and machine learning models for complex and unconventional real-life problems
- Strong mathematical and coding skills
- Proficiency in Python, MLOps (W&B, MLFlow) and ML packages (scikit-learn, PyTorch, JAX), along with SQL and AWS.
- Familiarity with ML workflow best practices.
- Interest in applications of machine learning in biotechnology
- Strong communication skills, both written and verbal
- Experience doing research and working with interdisciplinary teams
Additional Preferred Experience (desired, but not essential): - Experience in an industry setting related to biotechnology, chemicals, or materials manufacturing
- Experience with cheminformatics, computational chemistry, computational biology databases, data structures, material science and modelling package
Computer and modeling work required, this is an on-site position based in the Seaport of Boston, MA. 
