Data Scientist

Role Overview

Machine Learning Delivery

• Design and develop end-to-end machine learning solutions from data exploration and feature engineering through training, validation, and deployment.
• Integrate predictive and recommendation models into customer-facing and internal applications.
• Implement scalable pipelines with Databricks, PySpark, and Delta Lake.

Experimentation and MLOps

• Run A/B tests, uplift modeling, and causal inference to measure model performance and business impact.
• Operationalize models with CI/CD and MLOps practices covering versioning, monitoring, retraining, and governance.
• Monitor production systems for drift, performance degradation, and anomalies and apply explainability and fairness techniques.

Data Assets and Learning

• Contribute to feature stores and reusable data assets that accelerate experimentation and deployment.
• Stay current with machine learning, MLOps, and cloud data technologies.

Required Experience

• Extensive hands-on experience applying machine learning and statistical modeling in production or product-oriented environments.
• Understanding of traditional machine learning, deep learning architectures, and modern foundation models.
• Ability to design scalable machine learning pipelines and automate workflows with MLOps tools.
• Experience with Databricks, PySpark, distributed data frameworks, SQL, and feature engineering at scale.

Preferred Skills

• Python experience with Scikit-learn, AutoGluon, PyTorch, or TensorFlow is preferred.
• Familiarity with retrieval-augmented generation and large language model fine-tuning is a plus.
• Experience with real-time machine learning, forecasting, anomaly detection, or probabilistic modeling is a nice-to-have.
• Open-source contributions, technical blogging, or conference presentations are nice to have.

Continuous Learning