Amazon Data Scientist II Enterprise Engineering 

United States, Washington, Seattle 

626982044

You will join a highly motivated, collaborative and fun-loving team with an entrepreneurial spirit and bias for action. The role will challenge you to think differently, hone your skills, and invent at scale. We're looking for engineers who obsess over technical details but can delight customers by continually learning and building the right products. You will help to invent the future of advertising.
Technical Skills needed:-- Programming Languages: Proficiency in Python, including libraries such as TensorFlow, PyTorch, and scikit-learn.
- Experience with R or Java is a plus.
- Machine Learning and AI: Strong understanding of machine learning algorithms and frameworks. - Experience with natural language processing (NLP) techniques and models.
- Familiarity with reinforcement learning and its applications.
- Knowledge of supervised and unsupervised learning methods.
- Data Preprocessing and Analysis: Expertise in data cleaning, normalization, and transformation. Ability to perform feature engineering and selection. Proficiency in data analysis tools and techniques.
- Model Development and Evaluation: Experience in developing, training, and fine-tuning machine learning models. Knowledge of model evaluation metrics such as accuracy, precision, recall, F1-score, and AUC-ROC. Familiarity with cross-validation techniques.
- Big Data Technologies: Experience with big data tools and frameworks like Hadoop, Spark, or Kafka. Proficiency in handling large datasets and optimizing data pipelines.
- API and Microservices Development: Experience in developing and deploying RESTful APIs. Familiarity with microservices architecture and related technologies.
- Cloud Platforms: Experience with cloud platforms such as AWS. Proficiency in using cloud-based machine learning and data storage services.Key job responsibilities
1. Model Development: Design, develop, and implement machine learning models, particularly focusing on natural language processing (NLP) and reinforcement learning techniques.
2. Data Preprocessing: Perform data cleaning, normalization, and feature engineering to prepare datasets for model training.
3. Model Training: Train and fine-tune machine learning models to achieve high accuracy and robustness.5. Performance Evaluation: Use cross-validation and various performance metrics (e.g., precision, recall, F1-score) to evaluate model performance and ensure their reliability.
6. Continuous Improvement: Implement reinforcement learning strategies to ensure the AI system continuously learns and improves from user interactions.
7. Collaboration: Collaborate with data scientists, software engineers, and UX/UI designers to ensure the models meet user requirements and integrate seamlessly with existing tools.
8. Documentation: Document model architectures, training processes, and evaluation results to ensure transparency and reproducibility.

- 2+ years of data scientist experience
- 3+ years of data querying languages (e.g. SQL), scripting languages (e.g. Python) or statistical/mathematical software (e.g. R, SAS, Matlab, etc.) experience
- 3+ years of machine learning/statistical modeling data analysis tools and techniques, and parameters that affect their performance experience
- Experience applying theoretical models in an applied environment