Data Intelligence Engineer

Project C: Improve decision making through better data capture and integration processes

We are seeking a contractor to support the capture, storage and integration of Computational Drug Discovery generated in silico data to enable faster and more informed scientific decision making. This role will help reduce the cycle time from compound design to data visualization and analysis, with the goal of enabling near real-time feedback for novel design ideas.

The contractor will work closely with Computational Drug Discovery scientists to design and implement data structures and workflows that support model inventory, metrics and results storage.

Responsibilities

• Design and create database tables to support a model inventory, including ontology, model metrics, and model results.
• Enable ingestion and organization of results from a range of sources, including:
• custom machine learning models
• co-folding affinity prediction results
• custom MPO calculations
• other custom scientific calculations
• FEP+ results or related physics-based scoring
• Facilitate model containerization and deployment on the AID self-service platform to enable automatic API deployment.
• Build data ingestion workflows for result integration, including use of staging tables to manage inserts and updates of new data.
• If time permits: Develop scripts to enumerate virtual molecules using Free-Wilson or MMP transformation and compute associated predictions.
• Evaluate open-source models and methods as needed to support project goals.

1. Python, SQL and scripting

2. Database table creation and maintenance

3. Containerization and AWS EC2 environment (or similar) familiarity

4. Familiar with scientific data and machine learning, * Strong experience in data engineering, scientific data management, or computational chemistry/cheminformatics environments. * Proficiency with relational database design and schema development. * Experience creating and maintaining staging, integration, and data pipelines. * Working knowledge of machine learning models, model metadata, and result tracking frameworks. * Familiarity with containerization and deployment workflows such as Docker and API-based model serving. * Experience with scripting and automation, preferably in Python. * Familiarity with cheminformatics concepts such as Free-Wilson analysis, matched molecular pairs (MMPs), and virtual molecule enumeration. * Ability to evaluate open-source tools and models for scientific use cases. * Strong collaboration and communication skills for working across scientific and technical teams., * Experience supporting drug discovery or CDD-related data workflows. * Familiarity with FEP+ or related computational chemistry methods. * Exposure to ontology design and scientific data standardization. * Experience with cloud or platform-based self-service deployment environments. * Ability to work independently and deliver high-quality technical solutions in a contractor setting.