- Company Name
- ODAIA
- Job Title
- Machine Learning Engineer
- Job Description
-
Job title: Machine Learning Engineer
Role Summary
Lead the design, development, and deployment of advanced predictive and analytical machine learning models within a SaaS AI platform for pharma commercial teams. Own the full ML system lifecycle—from problem framing and data exploration to production deployment, monitoring, and iterative improvement—ensuring scalable, high‑quality solutions aligned with business goals.
Expectations
- Deliver production‑ready ML models that drive customer value and reduce time to therapy.
- Maintain rigorous testing, validation, and monitoring to sustain model performance.
- Communicate complex analytics to technical and non‑technical stakeholders.
- Continuously integrate cutting‑edge research and tools into existing workflows.
Key Responsibilities
- Develop and optimize predictive, time‑series, causal inference, and hierarchical models.
- Build end‑to‑end ML pipelines using Python, SQL, Dagster, and Kubernetes.
- Perform statistical validation, experiment design, hypothesis testing, and performance tuning.
- Monitor deployed models, diagnose drift, and iterate on improvements.
- Collaborate with data engineers, product managers, and business users to define requirements and translate them into data solutions.
- Document code, models, and experiments to enable reproducibility.
- Conduct knowledge sharing and mentorship within engineering and data science teams.
- Explore and incorporate emerging ML research and statistical techniques.
Required Skills
- 3+ years of experience building and deploying advanced ML models in production.
- Strong software engineering fundamentals: version control (Git), clean code, documentation, and collaborative workflows.
- Deep proficiency in Python with NumPy, Scikit‑learn, XGBoost, and PyTorch or similar.
- Expertise with SQL and modern in‑process databases (DuckDB, PostgreSQL, etc.).
- Hands‑on with workflow orchestration (Dagster, Airflow, etc.) and container orchestration (Kubernetes).
- Experience in cloud‑based ML environments (AWS, Azure, or GCP).
- Solid grounding in statistical validation, experimental design, and hypothesis testing.
- Excellent analytical and communication skills for multidisciplinary audiences.
Required Education & Certifications
- Bachelor’s or Master’s degree in Computer Science, Statistics, Data Science, or related field.
- Relevant certifications (e.g., AWS Certified Machine Learning – Specialty, Google Cloud Professional Data Engineer) are advantageous but not mandatory.