L1 - Junior Machine Learner - THE ORACLE

Wonderworth Solutions; Vellore; On-site; INR 600,000 - 900,000; Full time

Benefits offered by this job

ML training

Real operational data exposure

Exposure to ML lifecycle

Job summary

Wonderworth Solutions in Vellore, Tamil Nadu, invites applications for a Junior Machine Learner to work with the AI Intelligence Team on real business problems. You will participate in the full ML lifecycle: understand problems, prepare data, build models, validate, deploy, monitor, and explain results.

You should have a Bachelor's degree in a relevant field and at least 1 year of hands-on ML/DS experience with Python and ML libraries; familiarity with REST APIs and AWS is a plus.

Qualifications

• Bachelor's degree in Computer Science, AI, DS, Statistics, Mathematics, Engineering.
• 1+ year of experience in Machine Learning, Data Science, Python, or a related technical area.
• Proficiency in Python and ML libraries (Pandas, NumPy, Scikit-Learn).
• Experience with REST APIs (e.g., FastAPI) for serving predictions.
• Familiarity with PyTorch or TensorFlow and AWS.
• Active GitHub or Kaggle portfolio with real-world ML projects.

Responsibilities

• Collect, clean, and prepare data for ML projects.
• Build and evaluate machine learning models.
• Perform feature engineering and model testing.
• Document results, assumptions, and limitations.
• Support API-based deployment and model monitoring.
• Collaborate with senior team members to improve performance.
• Learn and apply ML best practices to real-world problems.

Skills

Problem solving

Learning ability

Team collaboration

Education

Bachelor's degree in Computer Science, AI, DS, Statistics, Mathematics, Engineering

Tools

Python

Git

Pandas

NumPy

Scikit-Learn

Matplotlib/Seaborn

REST APIs

PyTorch or TensorFlow

AWS

MongoDB

GitHub/Kaggle portfolio

Job description

Job Summary

L1 - Junior Machine Learner - THE ORACLE

Experienced

Vellore

Machine learning

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

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.

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.