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# Onsite_Mid/Sen Data Scientist (Không yêu cầu tiếng anh)

- **Company:** VMO LLC.
- **Location:** United States (Remote available)
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** Big Data, Information Engineering, Python (Programming Language), SQL Databases, Feature Engineering, Model Validation, Data Analytics, Xgboost, Build Tools, Machine Learning Operations
- **Published:** June 16, 2026
- **Apply:** https://arc.dev/remote-jobs/j/vmo-onsite_mid-sen-data-scientist-khong-yeu-c-u-ti-ng-anh-oxa6mkfkmz

## About the Role

* Strong Python - the existing model suite is Python-heavy
 * Advanced SQL large complex datasets
 * Proven propensity modelling experience - built and delivered, not just theoretical
 * Genuine hybrid capability - data science and data engineering
 * Confident communicator with non-technical stakeholders Hands-on with XGBoost, LightGBM

Nice to Have

 * Experience with MLOps and model deployment
 * Experience with AWS SageMaker
 * A/B testing and experimentation knowledge

## Description

The Data Scientist role focuses on building propensity models and sizing market opportunities for VMO project with banking clients. This hybrid position requires a blend of data science and engineering skills to create production-grade pipelines. The role is pivotal in driving sales strategy through data-driven outputs. Candidates should have a strong background in Python and SQL, along with proven experience in propensity modeling.

Propensity Modeling & Ranking:

 * Predict CTR and engagement probability
 * Use models like XGBoost / LightGBM
 * Optimize ranking using NDCG, MRR, Precision@K

Feature Engineering:

 * Build features from user behavior, interaction signals, and context
 * Handle sparse and noisy data

Cold Start Handling:

 * Design strategies for new users and new offers
 * Implement hybrid and fallback approaches

Bias Handling:

 * Mitigate popularity and exposure bias
 * Implement diversity and re-ranking strategies

Model Evaluation:

 * Define and track CTR, conversion, NDCG, AUC
 * Continuously improve engagement metrics

Scalability & Deployment:

 * Build systems for millions of users
 * Enable real-time or near real-time inference


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