Data Scientist

Stefanini North America and APAC • Contract • Remote (Newark, NJ, US) • 1d ago

Responsibilities:

Translate reporting requirements into modular, reusable Python classes and scripts.
Build automation workflows by using Python.
Read JSON input files.
Dynamically construct and execute SQL queries against our database.
Invoke calculation modules to cleanse, transform, and compute derived metrics.
Embed calculation outputs into specified PowerPoint slide shapes using python-pptx.
Upload completed presentations to Google Drive via the Google Drive API.
Implement robust error handling, logging, and retry mechanisms to ensure production reliability.
Parameterize and configure scripts to support different report types and inputs without code changes.
Use Git (or similar VCS) to manage deployment and scheduling of automated jobs.
Monitor script performance; optimize Python code for speed and scalability.
Document solution architecture, data flows, module interfaces, and configuration options.
Collaborate with stakeholders to validate results and refine requirements.
Stay up-to-date with Python best practices, key libraries (pandas, SQLAlchemy, python-pptx, google-api-python-client), and automation trends.

Required Qualifications:

Bachelors degree in Computer Science, Data Science, Engineering, or related field (or equivalent experience).
13 years of hands-on Python development, including object-oriented design.
Proven experience building Python-driven reporting or data-processing automations.
Proficient in Python data and automation libraries (e.g., pandas, NumPy, SciPy, python-pptx, google-api-python-client, etc.).
Hands-on experience integrating with Google APIs (Drive, Sheets, etc.).
Solid data analysis foundation: cleansing, transformation, aggregation, and basic statistics.
Proficient with Git or similar version control systems.
Excellent problem-solving skills and attention to detail.
Strong written and verbal communication; able to document and present technical solutions.

Preferred (Nice-to-Have)

SQL skills; comfortable writing and optimizing complex queries.
Familiarity with scheduling or orchestration tools (cron, Airflow, etc.).
Basic experience with Docker and CI/CD concepts.
Background in writing unit or integration tests for Python code.
Exposure to BI/reporting tools (Tableau, Power BI).

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