L1 Junior Machine Learner

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 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.

• 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 jobs@wonderws.co / 9042099690