Senior Data Scientist

Netlink; Madison Heights (MI); Sur place; USD 120 000 - 160 000; Plein temps

Résumé du poste

Netlink Software Group America, Inc. is seeking an experienced data scientist to design and deploy advanced recommender systems. You will build models for retention, cross-sell, and next-best-action, and implement scalable pipelines on Azure Databricks.

The role emphasizes deep learning architectures, embeddings, ranking, and MLOps, with ownership of the data science lifecycle from problem framing to production monitoring. Collaboration with engineering and business teams is essential.

Qualifications

• 5+ years of hands-on experience in Data Science and ML, including production-grade solutions.
• Advanced degree such as M.Tech, MS, MSc, or PhD in Computer Science, Computing, Mathematics, Statistics, or related quantitative discipline.
• Academic background from a Tier 1 or Tier 2 engineering, science, or technology institution.
• Strong expertise in deep learning and modern recommender-system architectures.
• Strong hands-on experience with Python, PySpark, SQL, PyTorch or TensorFlow, Azure Databricks, and MLflow.
• Strong understanding of embeddings, ranking, personalization, model evaluation, explainability, MLOps, and production deployment.
• Ability to independently architect and build complex ML solutions and work effectively with engineering and business teams.

Responsabilités

• Design and build recommendation models for retention, cross-sell, next-best-action, and quantity prediction.
• Develop advanced deep learning recommenders using neural collaborative filtering, two-tower architectures, deep embeddings, sequence-based models, and transformer-based approaches.
• Build customer, product, and interaction embeddings for personalized recommendations and ranking.
• Develop scalable feature engineering, training, evaluation pipelines on Azure Databricks.
• Design robust evaluation approaches including temporal validation, ranking metrics, explainability, and A/B testing.
• Productionize models with MLflow, automated retraining, model monitoring, drift detection, and CI/CD.
• Independently own the data science lifecycle from problem formulation through production deployment and continuous improvement.

Connaissances

Python

PySpark

SQL

PyTorch

TensorFlow

Azure Databricks

MLflow

Deep learning

Recommender systems

MLOps

CI/CD

Explainability

Formation

MS/ MSc / MSc / PhD in Computer Science or related quantitative field

Outils

Azure Databricks

MLflow

Description du poste

Key Responsibilities :

• Design and build recommendation models for retention, cross-sell, next-best-action, and quantity prediction.
• Develop advanced deep learning recommenders using neural collaborative filtering, two-tower architectures, deep embeddings, sequence-based models, and transformer-based approaches.
• Build customer, product, and interaction embeddings for personalized recommendations and ranking.
• Develop scalable feature engineering, training, evaluation, and inference pipelines on Azure Databricks.
• Design robust evaluation approaches including temporal validation, ranking metrics, explainability, and A/B testing.
• Productionize models with MLflow, automated retraining, model monitoring, drift detection, and CI/CD.
• Independently own the data science lifecycle from problem formulation through production deployment and continuous improvement.

Required Profile:

• 5+ years of hands-on experience in Data Science and Machine Learning, including production-grade ML solutions.
• Advanced degree such as M.Tech, MS, MSc, or PhD in Computer Science, Computing, Mathematics, Statistics, or a related quantitative discipline.
• Academic background from a Tier 1 or Tier 2 engineering, science, or technology institution.
• Strong expertise in deep learning and modern recommender-system architectures.
• Strong hands-on experience with Python, PySpark, SQL, PyTorch or TensorFlow, Azure Databricks, and MLflow.
• Strong understanding of embeddings, ranking, personalization, model evaluation, explainability, MLOps, and production deployment.
• Ability to independently architect and build complex ML solutions and work effectively with engineering and business teams.