Hydra Developer (Python / Pharma /Life Sciences)

W2 or 1099 independent only

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We are seeking an experienced Hydra Developer to design, build, and scale configuration-driven applications supporting advanced analytics, machine learning, and scientific workflows in a regulated pharmaceutical environment.

This role will focus on leveraging Hydra to manage complex, parameterized workflows, enabling reproducibility, scalability, and rapid experimentation across R&D, clinical, and data science use cases.

Hydra is an open-source Python framework used to configure complex applications through hierarchical, composable configurations and command-line overrides, making it ideal for research and ML-heavy environments.

Key Responsibilities

Hydra / Python Development

• Design and implement configuration-driven Python applications using Hydra and OmegaConf
• Build modular, composable configuration structures (YAML-based) to support:
• ML experimentation pipelines
• Data processing workflows
• Simulation and modeling environments
• Enable multi-run experimentation (parameter sweeps, scenario testing) using Hydra’s job launching capabilities
• Develop reusable components leveraging Hydra’s ability to instantiate objects dynamically from configuration
• Integrate Hydra with:
• ML frameworks (PyTorch, TensorFlow, Scikit-learn)
• Experiment tracking tools (e.g., W&B, MLflow)
• Cloud / HPC execution environments
  
  

Pharma / Life Sciences Application Development

• Build and support solutions across:
• Clinical data pipelines (e.g., SDTM/ADaM processing)
• Regulatory submission workflows
• Pharmacovigilance / safety analytics
• R&D data science platforms
• Ensure solutions meet GxP, 21 CFR Part 11, and validation requirements
• Support inspection-ready workflows with reproducibility and audit trails
• Collaborate with:
• Clinical Data Scientists
• Biostatisticians
• Regulatory Affairs teams
• Digital / R&D IT
  
  

Architecture & Engineering

• Architect systems that separate code and configuration, improving maintainability and reproducibility
• Implement CI/CD pipelines for ML and data workflows
• Optimize performance for large-scale data and compute workloads
• Ensure robust logging, monitoring, and traceability (leveraging Hydra’s built-in logging capabilities)
• Contribute to platform standardization for experiment configuration and execution
  
  

Required Qualifications

Technical Skills

• Strong Python development experience (5 years preferred)
• Hands-on experience with Hydra and OmegaConf
• Experience building config-driven or parameterized applications
• Familiarity with:
• YAML-based configuration systems
• Object instantiation patterns (dependency injection via config)
• Experience with ML/data tools:
• PyTorch / TensorFlow / Scikit-learn
• Pandas / NumPy
• Knowledge of:
• REST APIs, ETL pipelines, and data orchestration
• Git, CI/CD, containerization (Docker/Kubernetes)
  
  

Pharma / Domain Experience

• 3 years in pharmaceutical, biotech, or CRO environments
• Experience with at least one:
• Clinical data systems (e.g., Medidata, Veeva, SAS)
• Regulatory systems (e.g., RIM, submissions workflows)
• Safety systems (e.g., Argus)
• Understanding of:
• GxP validation
• Data integrity principles (ALCOA )
• Audit/inspection readiness
  
  

Preferred Qualifications

• Experience using Hydra for:
• ML experimentation platforms
• Hyperparameter sweeps / distributed runs
• Familiarity with:
• Experiment tracking tools (MLflow, Weights & Biases)
• Workflow orchestration (Airflow, Prefect)
• Experience in AI/ML applied to drug discovery, clinical trials, or real-world evidence
• Exposure to cloud platforms (AWS, Azure, GCP)
  
  

Key Competencies

• Strong problem-solving in complex, parameterized systems
• Ability to bridge data science and engineering
• Experience working in regulated environments
• Strong communication with both technical and scientific stakeholders
  
  

Example Use Cases This Role Will Support

• Configurable clinical data ingestion pipelines across studies
• Reproducible statistical analysis workflows
• Scalable AI/ML experimentation frameworks for drug discovery
• Automated regulatory reporting pipelines