- Company Name
- Salud Revenue Partners
- Job Title
- Machine Learning Engineer
- Job Description
-
**Job Title:** Senior Machine Learning Engineer / Data Scientist
**Role Summary:**
Design, develop, and deploy production‑ready machine‑learning solutions—including predictive models, natural‑language processing (NLP) pipelines, and recommendation engines—to improve revenue‑cycle performance for healthcare providers. Collaborate with data engineering, product, and business teams to integrate AI into operational workflows and maintain model performance over time.
**Expectations:**
- Deliver high‑impact AI models that meet accuracy, scalability, and reliability targets.
- Write clean, testable code and follow MLOps best practices (version control, containerization, monitoring).
- Communicate results and technical concepts clearly to both technical and non‑technical stakeholders.
- Participate in on‑site meetings and collaborative sessions while operating in a hybrid work environment.
**Key Responsibilities:**
1. Build and fine‑tune structured‑data models for forecasting, classification, and risk scoring.
2. Develop NLP solutions for document classification, entity extraction, and semantic understanding using traditional methods and transformer‑based architectures.
3. Create recommendation systems (collaborative, content‑based, hybrid) to support personalization and content ranking.
4. Partner with data engineering to acquire, clean, and pipeline structured and unstructured data.
5. Implement MLOps pipelines (Docker, MLflow, Airflow) for model versioning, deployment, and monitoring.
6. Deploy models into production environments, track performance, schedule retraining, and troubleshoot issues.
7. Document model design, datasets, experiments, and outcomes; ensure reproducibility and alignment with business objectives.
**Required Skills:**
- **Programming:** Advanced Python; libraries: scikit‑learn, PyTorch, TensorFlow, Hugging Face.
- **MLOps:** Docker, MLflow, Airflow (or equivalent), Git.
- **Modeling:** Experience in at least two domains: structured prediction, NLP, recommendation systems.
- **Data Handling:** SQL/NoSQL, data cleaning, feature engineering for both structured and unstructured data.
- **Communication:** Strong verbal and written skills; ability to convey technical concepts to diverse audiences.
- **Collaboration:** Team player comfortable working with cross‑functional and remote teams.
**Required Education & Certifications:**
- Master’s degree or Ph.D. in Data Science, Computer Science, Engineering, or a related quantitative field.
- No specific certifications required, but demonstrated expertise with the above tools and techniques is essential.
West lafayette, United states
Hybrid
Junior
22-10-2025