We are seeking a talented Machine Learning Engineer (MLE) to join our team. The MLE will be responsible for developing and implementing machine learning models and algorithms. The ideal candidate should have a strong understanding of a wide variety of altos like Naive Bayes, and K Nearest Neighbors (KNN), and experience in setting up data hub pipelines, including producer-consumer architecture. Familiarity with external A/B testing tools and GORSE.
- Develop and implement machine learning models and algorithms, with a focus on Naive Bayes and K Nearest Neighbors (KNN) for various projects.
- Design and set up data hub pipelines using a producer-consumer architecture, ensuring efficient and scalable data processing.
- Collaborate with data engineers to ensure the availability, reliability, and performance of data pipelines, ensuring high-quality data for machine learning models.
- Utilize external A/B testing tools to evaluate and optimize model performance, and provide insights and recommendations based on the results.
- Work closely with the product and engineering teams to understand business requirements and translate them into data-driven solutions.
- Implement and optimize GORSE to improve the accuracy and efficiency of recommendation algorithms.
- Stay up-to-date with the latest trends and advancements in machine learning, data engineering, and related technologies, and apply them to enhance our models and systems.
- Bachelor's or higher degree in Computer Science, Data Science, or a related field.
- Solid understanding and practical experience with the implementation of predictive algorithms.
- Proficiency in setting up data hub pipelines using a producer-consumer architecture.
- Familiarity with external A/B testing tools to evaluate and optimize machine learning models.
- Experience with GORS (Generalized Online Recommendation System) implementation and optimization.
- Strong programming skills in languages such as Python or R, along with experience with relevant frameworks and libraries (e.g., TensorFlow, PyTorch, Scikit-learn).
- Proficient in SQL/NoSQL, and which is more importantly the grasp of difference.
- Strong analytical and problem-solving skills, with the ability to analyze complex data sets and derive actionable insights.
- Excellent communication and collaboration skills, with the ability to work effectively in a cross-functional team.
- Master's or PhD degree in Computer Science, Data Science, or a related field.
- Experience with big data technologies and distributed computing frameworks (familiar with services like AWS Sage Maker, and Google AutoML).
- Knowledge of cloud platforms and services (e.g., AWS, Google Cloud).
- Familiarity with deep learning algorithms and frameworks.
- Experience in deploying and managing machine learning models in production environments.
- Preferred industry experience in gaming or e-commerce.
- Specific skill in rapid prototyping and agile development methodologies.
- Familiarity with the latest trends in AI and machine learning, especially as they apply to page optimization and conversion improvement.