Head/Expert Data Scientist

Mô tả Công việc
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 and train 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.
III. Team Leadership & Mentorship:
Provide technical leadership and guidance to junior data scientists, fostering a culture of collaboration, innovation, and continuous learning within the team.
Conduct code reviews, mentor team members on best practices for data science workflows, and contribute to the development of internal standards and guidelines.
Stay up-to-date on the latest advancements in the field and actively explore new technologies and methodologies to enhance the team's capabilities.
IV. Cross-Functional Collaboration & Communication:
Collaborate effectively with business stakeholders, product managers, engineers, and other teams to translate business requirements into technical solutions and ensure alignment with overall business goals.
Communicate technical concepts and findings clearly and concisely to both technical and non-technical audiences, using data visualization and storytelling techniques
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.
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 and train 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.
Provide technical leadership and guidance to junior data scientists, fostering a culture of collaboration, innovation, and continuous learning within the team.
Conduct code reviews, mentor team members on best practices for data science workflows, and contribute to the development of internal standards and guidelines.
Stay up-to-date on the latest advancements in the field and actively explore new technologies and methodologies to enhance the team's capabilities.
Collaborate effectively with business stakeholders, product managers, engineers, and other teams to translate business requirements into technical solutions and ensure alignment with overall business goals.
Communicate technical concepts and findings clearly and concisely to both technical and non-technical audiences, using data visualization and storytelling techniques