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# Senior Specialist, Data Analysis

- **Company:** Merck Sharp & Dohme LLC
- **Location:** Cambridge, MA, United States
- **Experience:** Expert
- **Salary:** $159,600.0 - $251,200.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Data Analysis, Bioinformatics, Cloud Computing, Computational Biology, Computer Simulation, Databases, Information Engineering, Data Visualization, Python (Programming Language), KNIME, Linux System Administration, Machine Learning, Tensorflow, SQL Databases, High Performance Computing, Pytorch, Keras, Pandas, Scikit Learn, Information Technology, Data Analytics, Data Management, Spotfire, Software Library, Databricks
- **Published:** October 4, 2026
- **Apply:** https://dejobs.org/x/x/E9C284D59A804544A3F651152369FEC3/job/

## About the Role

We are seeking an exceptional and highly motivated informatician to join the Cheminformatics group within Modeling and Informatics (M&I) at our our company. This role requires strong interpersonal and problem-solving skills and experience working in a cross-functional team environment. It involves both the development of innovative computational methods involving AI/ML and cheminformatics, as well as direct application of advanced computational approaches to the invention of novel medicines., Ph.D. in cheminformatics, bioinformatics, computational biology or chemistry, computer science, statistics or related fields, * Experience applying cheminformatics or AI/ML approaches to problem-solving in drug discovery.

 * Broad understanding of modern data science methods, particularly machine learning, e.g., predictive modeling, active learning, explainable ML.
 * Strong oral and written communication skills, as well as problem-solving skills.
 * Track record of scientific publications in peer-reviewed journals.
 * Proficiency in scripting (Python and/or R), SQL, and cheminformatics toolkits (OEChem, RDKit, DeepChem).

Preferred Experience and Skills:

 * Demonstrated ability to develop and apply AI/ML to problems of chemical or biological interest.
 * Experience with IT infrastructure such as database systems, cloud computing (such as Databricks, AWS), and high-performance computing in Linux environments.
 * Expertise in machine learning libraries (TensorFlow, Keras, PyTorch, Pandas, Scikit-Learn).
 * Experience with workflow applications and data analytics software (Pipeline Pilot, Knime, Spotfire).
 * Experience with high throughput screening data analysis methods.
 * Experience in chemical and biological signature identification and/or biomarker discovery.
 * Familiarity with or experience with multiple therapeutic modalities (small molecules, peptides, ADCs, etc.)
 * Experience working in multidisciplinary teams that include experimental scientists., Academic Research, Algorithms, Biological Data Analysis, Biomarker Discovery, Biovia Pipeline Pilot, Chemical Informatics, Computational Biology, Data Analysis, Data Management, Data Modeling, Data Visualization, Drug Discovery Research, KNIME, Machine Learning (ML), Python (Programming Language), Scientific Research, Scientific Writing, TIBCO Spotfire, Virtual Screening

## Description

* Implement and apply effective AI/ML and cheminformatics workflows to impact early target discovery space data analysis.
 * Analyze, interpret, and summarize complex chemical biology data and utilize a variety of data visualization tools to communicate findings.
 * Deliver optimized informatics algorithms and workflows to expedite drug discovery pipelines in the early target discovery space for small molecules, peptides, and targeted protein degraders.
 * Leverage novel ML, statistics, and cheminformatics techniques to improve hit identification for different screening paradigms.
 * Analyze and integrate internal and external knowledge to facilitate biological and chemical signature identification.
 * Proactively engage with cross-functional teams of experimental and computational scientists and collaborate effectively to advance project and pipeline goals
 * Continue research in areas of AI/ML and cheminformatics relevant to drug discovery, and evaluate new technology and software from commercial sources, open innovation, or external collaborations.
 * Effectively communicate scientific results internally and externally through presentations and publications.
 * Share AI/ML and/or data engineering knowledge to augment team skills and capabilities.