CV chính/CVCC Data Scientist

I. Data Acquisition & Feature Engineering:

• Collaborate with stakeholders to identify and prioritize high-value data sources, including internal databases, external APIs, open data sets, web scraping, and online/offline behavioral data.
• Design, implement, and maintain robust and scalable data pipelines for ingesting, processing, and transforming data from diverse sources, ensuring data quality, consistency, and security.
• Conduct in-depth exploratory data analysis to understand data patterns, identify potential biases, and uncover valuable insights.
• Develop advanced feature engineering techniques, including creating new features from raw data, feature selection, and dimensionality reduction, to enhance model performance.
• Design and implement a Feature Store to manage and share features across multiple projects and teams, ensuring consistency and reusability.

II. Model Development & Deployment:

• Research, select, and implement machine learning and deep learning algorithms and architectures for a variety of business applications, including predictive modeling, classification, clustering, and recommendation systems, targeted marketing, ads exchange.
• Build high-performance models using appropriate tools and frameworks (focus on Vertex AI and CDP customer data platform), optimizing for accuracy, scalability, and interpretability.
• Develop and deploy models in production environments, using containerization technologies (e.g., Docker), cloud platforms, and APIs.
• Continuously monitor model performance, identify and address performance degradation, and implement strategies for model retraining and updates.

• Degree in a quantitative field such as Computer Science, Statistics, Mathematics, Operations Research, or a related field.
• 2+ years of experience in a senior data science or machine learning role, with a proven track record of developing and deploying production-ready models.
• Deep understanding of statistical modeling, machine learning algorithms, and data mining techniques, including experience with deep learning, natural language processing, and/or computer vision.
• Expert-level proficiency in Python and relevant data science libraries .
• Extensive experience with building and deploying data pipelines and machine learning models on cloud platforms (e.g, GCP, CDP, real time) and big data technologies (e.g., Spark, flink).
• Strong software engineering skills, including experience with version control (e.g., Git), testing, and CI/CD.
• Excellent communication, presentation, and interpersonal skills, with the ability to translate complex technical concepts into clear, actionable insights for diverse audiences.