Data Scientist Assistant

Bachelor's degree in computer science-related fields (e.g., Data Science, Data Engineering, Computer Science). Python programming proficiency is required. Solution-oriented mindset with the ability to apply creative problem-solving techniques. Excellent communicator able to engage effectively across all organizational levels—including in virtual environments—and translate complex technical concepts into clear, plain language. Collaborative team player, able to work effectively in cross-functional teams and contribute to shared goals in a virtual environment. Flexible and adaptable, comfortable working in dynamic environments and adjusting to changing priorities. Proficiency in database technologies such as MySQL, MS SQL Server, or Elasticsearch. Understanding of generative AI tools and their practical applications. Experience with Python web frameworks (e.g., Django, Flask) is preferred, but not required. Knowledge of statistical software like R, SAS, or STATA is preferred, but not required. Familiarity with GitHub workflows (e.g., pull requests, branch protection rules), GitHub Actions for CI/CD pipelines, and automated pre-commit hooks is beneficial. Familiarity with code formatting and linting tools (e.g., Ruff, Black) is beneficial. Interest in exploring and developing innovative tools for data-driven applications.

Assist in designing and building cloud applications and data pipelines using Platform-as-a-Service (PaaS) and open-source tools, while adhering to coding best practices for quality and consistency. Develop data visualizations, dashboards, and reports using open-source or Business Intelligence (BI) tools to support insights and decision-making. Collect, validate, and analyze data through standard programming languages and statistical techniques, including modeling, simulation, and forecasting with common software packages. Develop and deploy advanced artificial intelligence solutions, including natural language processing, computer vision, automatic speech recognition, and generative AI. Ensure data accuracy and integrity for assigned projects, while contributing to moderately complex tasks under indirect supervision. Collaborate with stakeholders to communicate project needs, leverage external resources, and organize deliverables effectively.