The Role:
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:
Understand Problems Prepare Data Build Models Validate Deploy Post-deployment Monitor Explainability

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
TARGET: 90% Model Validation Success 90% On-Time Delivery
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 easy: 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.
ML Libraries & Frameworks: Pandas, NumPy, Scikit-Learn, Matplotlib/Seaborn.
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.
Active GitHub profile or Kaggle portfolio demonstrating real-world ML projects.
Understanding of basic database management systems (MongoDB).
Strong problem-solving and learning ability.

Contact us on / (phone hidden)

📌 Junior Machine Learner (Vellore)
🏢 Wonder Worth Solutions
📍 Vellore