Need Kaggle Expert for Search Relevance Competition (Current LB 0.903)

I am participating in a private Kaggle e-commerce search relevance competition and currently have a public leaderboard score of 0.903. The task is binary query-product relevance classification. The training data contains only positive labels, and the test set contains approximately 3.36 million query-product pairs. The evaluation metric is Macro F1. I already have an advanced working solution involving: - Semantic retrieval models - NLP ranking and reranking - LLM-based judging systems - Consensus-based label correction - Promote/demote label-flip strategies - Pseudo-labeling - Ensemble methods - Self-hosted open-source models I am NOT looking for a beginner machine learning tutor or someone to build a basic baseline from scratch. I need an experienced Kaggle competitor, preferably Kaggle Expert, Master, or Grandmaster level, who can review my current solution and identify the highest-impact improvements. Main goals: 1. Review my current pipeline, submissions, and experiment history. 2. Identify the main bottleneck after reaching a 0.903 leaderboard score. 3. Evaluate whether my consensus label-flip strategy is statistically sound. 4. Review my validation methodology and identify possible leaderboard overfitting. 5. Suggest the top 3–5 experiments most likely to improve the score. 6. Provide a concrete and prioritized action plan that can be executed quickly. Important technical constraint: The final solution must be fully reproducible offline and self-hosted. Paid APIs, free hosted APIs, commercial model endpoints, closed-source services, and any method that depends on an external online inference service are not allowed. The final predictions must be reproducible later without internet access. Therefore, all recommendations must be based on: - Open-source models - Downloadable model weights - Local or self-hosted inference - Offline reproducible pipelines - Reproducible preprocessing and prediction code Recommendations that depend on OpenAI, Anthropic, Google, proprietary hosted models, paid APIs, free APIs, or non-reproducible online services will not be considered. Preferred experience: - Kaggle competitions - NLP - Search relevance - Information retrieval - Ranking and reranking systems - Pseudo-labeling - Ensemble methods - Weak supervision or positive-unlabeled learning - Offline inference and self-hosted open-source models Expected deliverables: - A review of my current approach - Identification of the most important remaining bottleneck - A critique of the current validation and label-flip strategy - A ranked list of the highest-impact next experiments - A practical short-term action plan - Recommendations that comply with the offline and self-hosted requirement When applying, please include: - Your Kaggle profile - Your highest Kaggle rank - Competition medals or notable results - Relevant NLP, retrieval, ranking, or recommendation experience - Experience with pseudo-labeling and ensemble methods - Experience building offline and self-hosted machine learning pipelines This is initially a small consulting and solution-audit task. If the collaboration is successful, additional work may follow. Applications without a Kaggle profile or relevant competition experience will not be considered.