AI Kaggle Specialist

D’SIAR TECH

We are looking for driven students and practitioners who want real-world competitive experience, visibility, and direct equity/prize participation—not a typical internship or stipend-based role.

This role is ideal for candidates who want to:

• Compete seriously on Kaggle
• Build a winner-grade portfolio

Learn advanced ML strategies used in real competitions

• Earn through competition winnings and equity participation

Key Responsibilities

• Participate in active Kaggle competitions under the D’SIAR TECH banner
• Perform Data exploration & feature engineering
• Perform Model building (ML / DL / ensemble methods)
• Perform Cross-validation & leaderboard optimization
• Cross-validation & leaderboard optimization

Collaborate with mentors and teammates on:

• Strategy planning
• Error analysis
• Model stacking & inference optimization
• Document approaches and solutions for internal learning assets

Required Skills

Strong fundamentals in:

• Python
• Pandas, NumPy, Scikit-learn

Understanding of:

• Machine Learning algorithms
• Evaluation metrics & cross-validation

Familiarity with:

• Kaggle notebooks & submissions
• Feature engineering techniques

Mindset matters more than years of experience

Good to Have

Prior Kaggle participation (any rank is fine)

Experience with:

• XGBoost, LightGBM, CatBoost
• Deep Learning (PyTorch / TensorFlow)

Knowledge of:

• Model ensembling & stacking
• Public vs Private leaderboard strategies

Compensation Model (Important – Please Read)

This is a performance-based role, not a salaried position.

Equity / Revenue Share based on:

• Kaggle prize winnings
• Competition ranking & contribution

Team members share winnings transparently

Top performers may be offered:

• Long-term roles at D’SIAR TECH
• Lead positions in future competitions
• Mentorship & recommendation support

Who Should Apply

• Students passionate about winning ML competitions
• Early-career ML engineers seeking real proof of skill
• Kaggle enthusiasts who want to move from practice → podium