Develop robust data pipelines for acquiring, cleaning, and preprocessing large-scale datasets from various sources.
Implement strategies for data quality assessment and assurance to ensure reliable analysis outcomes.
Conduct comprehensive exploratory data analysis to uncover patterns, trends, and insights within the data.
Create interactive visualizations and dashboards to effectively communicate findings to stakeholders.
Design, develop, and deploy predictive models using advanced machine learning algorithms and techniques.
Optimize model performance through feature engineering, hyperparameter tuning, and model selection.
Build scalable and efficient software solutions for deploying machine learning models into production environments.
Integrate data science workflows with existing systems and applications to enable seamless data-driven decision-making.
Establish monitoring mechanisms to track the performance of deployed models and identify opportunities for improvement.
Collaborate closely with cross-functional teams including data engineers, software developers, and business stakeholders.

Bachelor's or Master's degree in Computer Science, Data Science, Statistics, or a related field.
Proven experience in data preprocessing, exploratory data analysis, and feature engineering.
Expert-level skills in data visualization platforms (e.g. Qlik, Tableau, Power BI)
Proficiency in programming languages such as Python, R, and SQL for data manipulation and analysis.
Strong understanding of machine learning algorithms and statistical modeling techniques.
Hands-on experience with machine learning libraries/frameworks such as TensorFlow, PyTorch, scikit-learn, etc.
Experience in developing and deploying end-to-end data science solutions in cloud environments (e.g., AWS, Azure, GCP).
Solid understanding of software engineering principles and best practices for building scalable and maintainable code.

Full Stack Data Scientist

Key skills