Contractor - Data Science Course Maintenance Specialist (School of Data Science)

Udacity, now an Accenture company, is seeking qualified contractors to join their School of Data Science contractor pool. This role involves maintaining and updating high-quality, industry-aligned courses across the end-to-end data lifecycle, including:

• Data Literacy and Data Fluency
• Data Analytics and Business Analytics (SQL, Spreadsheets, Power BI, Tableau)
• Data Visualization and Data Storytelling
• Statistics, Probability, and Experimental Design
• Data Science and Machine Learning Fundamentals (Python, R, ML pipelines)
• Data Engineering and Streaming (Airflow, Kafka, Spark, Data Lakes/Lakehouses, Data Warehouses)
• Data Architecture, Data Governance, and Data Privacy
• Cloud Data Solutions on AWS and Azure (e.g., Redshift, Synapse, Databricks, S3/ADLS)

Contractors will work closely with Udacity’s team to ensure courses remain technically accurate, pedagogically sound, and aligned with industry practices.

Responsibilities:

1. Analyze course performance metrics (lesson ratings, pass rates, and qualitative feedback) to identify content requiring updates.
2. Review student feedback at scale to prioritize actionable improvements.
3. Bug-fixes:
   • Update classroom instructions to reflect the latest data tools, cloud UI changes (AWS, Azure), and library behavior.
   • Debug and update code in Jupyter notebooks, VS Code workspaces, and SQL exercises (Python, SQL, and occasionally R).
   • Fix broken queries, incorrect visualizations, outdated screenshots, and mismatched expected outputs.
4. Enhancements:
   • Update text, screenshots, diagrams, and examples to reflect current best practices.
   • Refresh tutorials, exercises, and projects to use modern data workflows, APIs, or libraries (newer versions of pandas, scikit-learn, PySpark, or BI tools).
   • Improve project rubrics, starter code, and datasets for clarity and robustness.
5. Workspace and environment updates:
   • Update Udacity Workspaces using self-service Studio (in-house tool).
   • Install and validate updated Python and R packages in existing workspaces.
   • Update exercises and project starter code to support newer programming environments (e.g., upgrading older Python versions, or updating SQL dialect usage to match the current engine).
6. Cloud Lab validation and troubleshooting:
   • Test the Cloud Labs used for data engineering and architecture content.
   • Verify that necessary cloud services (e.g., data warehouses, storage accounts, streaming services, compute) are enabled and properly configured.
   • Troubleshoot student access issues and permission-related problems in federated cloud accounts (AWS and Azure), particularly around data access, IAM/RBAC, and resource usage.

Required Skill Set:

• Strong understanding of core data skills:
  • Data wrangling, exploratory data analysis, and basic statistics.
  • Writing and optimizing SQL queries.
  • Building and interpreting data visualizations and dashboards.
• Hands-on experience with Python for data (pandas, NumPy, scikit-learn, visualization libraries, basic ML) and/or R for data analysis.
• Familiarity with at least one BI or visualization tool, such as Power BI or Tableau.
• Familiarity with foundational cloud data services on AWS or Azure (e.g., S3/ADLS, data warehouses, basic compute, and storage patterns).
• Familiarity with containerization technologies (Docker) and notebook-based workflows (Jupyter).
• At least 2 years of professional experience in a data-related role (e.g., Data Analyst, Data Scientist, Data Engineer, BI Developer).
• Experience working with version control systems (Git/GitHub).
• Ability to debug and update Python-based and SQL-based exercises and projects.
• Strong troubleshooting skills to resolve student-reported issues efficiently (e.g., environment mismatches, package conflicts, SQL errors, visualization failures).
• Excellent written communication skills for documenting changes and providing clear, step-by-step instructions.
• Ability to write high-quality instructional artifacts and technical documentation modeled on industry standards.
• Strong attention to detail with a student-first mindset.
• Experience with statistics or machine learning in production environments is a plus.
• Cloud or data-related certifications (e.g., AWS Data/Analytics, Azure Data Engineer) are a plus.