- As a Junior Machine Learner , you will work with the AI & Intelligence Team to build practical machine learning solutions for real business and operational problems.
- You will work across the ML lifecycle:
What You Will Own – Your Responsibilities :
- Collect, clean, and prepare data for ML projects.
- Perform basic data analysis and identify patterns.
- Build and evaluate machine learning models.
- Perform feature engineering and model testing.
- Document model results, assumptions, and limitations.
- Support API-based deployment and model monitoring.
- Work with senior team members to improve model performance.
- Learn and apply ML best practices to real-world problems.
Non-Negotiable Metric :
- MODEL QUALITY + DELIVERY + OPERATIONAL IMPACT
- Measured through data quality, model performance, validation results, delivery timelines, documentation, and measurable improvement in assigned operational outcomes.
The Fight: What You will Fight:
- Intelligence without impact. A model is valuable only when it helps solve a real problem. You will fight poor data, weak validation, unnecessary complexity, and models that perform well in testing but fail to deliver practical value.
- Your goal is simple: build reliable intelligence that can be understood, used, and improved
WHO THRIVES HERE : Three Traits We Cannot Teach
- You measure impact, not accuracy.
- You govern your own work.
- You build to be understood.
THE WWS PERKS :
- Training in ML, RCM domain knowledge, and production deployment .
- Opportunity to work with real operational data and business problems .
- Exposure to the complete ML development lifecycle .
- Collaboration with AI, Technology, and Operations teams .
Qualifications:
- Bachelor’s degree in Computer Science, AI, Data Science, Statistics, Mathematics, Engineering , or a related field.
- 1+ year of experience in Machine Learning, Data Science, Python, or a related technical area.
- Programming Languages & Tools: Python, Git.
- Core Concepts: Data preprocessing, feature scaling, regression, classification, clustering, model evaluation metrics, and basic statistics.
- API Integration: Basic knowledge of REST APIs (e.g., FastAPI) for serving model predictions.
- Practical exposure to PyTorch or TensorFlow frameworks.
- Familiarity with the cloud service AWS.
- Un derstanding of basic database management systems (MongoDB).
- Strong problem-solving and learning ability.
WWS Offers A Wide Range Of Services That Give Our Clients The Complete Platform To Comprehending The Entire Medical Practise Workflow. Wws Is Now One Of The Fastest Growing Revenue Cycle Management Companies In The United States.
Wonder Worth Solutions LLC
2711, Centerville Road, Suite 400, Wilmington, Delaware, United States – 19808.