Bristol-Myers Squibb Company

Molecular Analytics Scientist

Princeton, NJ, US
Apr 12, 2018
Required Education
Position Type
Full time
Bristol-Myers Squibb is a global biopharmaceutical company whose mission is to discover, develop and deliver innovative medicines that help patients prevail over serious diseases.

One shared journey is moving us forward at Bristol-Myers Squibb. Around the world, we are passionate about making an impact on the lives of patients with serious disease. Empowered to apply our individual talents and ideas so that we can learn and grow together. Driven to make a difference, from innovative research to hands-on community support. Bristol-Myers Squibb recognizes the importance of balance and flexibility in our work environment. We offer a wide variety of competitive benefits, services and programs that provide our employees the resources to pursue their goals, both at work and in their personal lives.

Build a capability within Molecular Analytics that would serve multiple roles. This is based on our own strategy and positive feedback we received during the Chemistry IT/Cheminformatics breakout and survey we conducted a few years ago.

Anticipated Cheminformatician Role in the Strategy:

• Embedded with a traditional CADD rep onto key projects.

• Serve as an on-call resource for "expert data analysis" by project teams

• Engage in machine learning & data mining for cross-project efforts & tools (e.g. matched pairs, liability models, deck enhancement, etc.)

Contribute to an emerging integration platform that will leverage data science,, cheminformatics, and bioinformatics for the identification and optimization of protein and small molecule therapeutics. Example workflows or tools include:

• HTS data analysis for hit identification using statistical analysis, visual inspection, and structure/sequence interrogation

• Annotated databases linking molecules to targets to pathways for use in target identification

• Multiparametric design using structure-based or data mining models of key ADMET and selectivity endpoints

• Machine learning and data mining models for leveraging matched molecular pairs, assay data, crystallographic databases, etc.

• Visualizations and deep learning models of immunogenicity, stability, etc. for the design of protein therapeutics


• In-depth knowledge of cheminformatics, computer-aided drug design, computational chemistry or computational biology;

• A history of impact using computational approaches to drive scientific experiment and design

• Strong knowledge of chemical and protein structure and sequence

• Background in methods for statistical analysis, cheminformatics, bioinformatics, machine learning or data visualization

• Competency in programming, scripting and databases required (e.g. C, C++, JavaScript, Python, R, MySQL, Oracle, MongoDB)

• An aptitude and desire to learn and apply new techniques is expected

• A basic knowledge of medicinal chemistry, biology and ADMET/PK

• Personal attributes of integrity, creativity, problem solving, and strong work ethic

• Ability to work within a multidisciplinary team across geographic areas to advance discovery projects

• Excellent communication skills are a must

• Ph.D. in Chemistry, Biology, or Computer Science with 0-3 years of postdoctoral experience or commensurate career experience