On-Call Maintenance Specialist, Data Science - Contract Role

Accountabilities: Analyze course performance data, including lesson ratings, completion or pass rates, and qualitative learner feedback, to identify content that requires maintenance or improvement. Review student feedback at scale and prioritize actionable technical and instructional updates. Troubleshoot and resolve issues across Python, SQL, R, notebooks, visualizations, exercises, projects, and technical documentation. Update course instructions, examples, screenshots, diagrams, queries, expected outputs, and other materials to reflect current tools, interfaces, libraries, and industry practices. Refresh exercises and projects using modern data workflows, APIs, libraries, and technologies, including tools such as pandas, scikit-learn, PySpark, and business intelligence platforms. Improve project rubrics, starter code, datasets, and learning materials to increase clarity, reliability, and robustness. Maintain browser-based learning environments, including Jupyter, SQL, and VS Code workspaces, by validating packages, programming environments, and tool compatibility. Update exercises and starter code to support newer Python or SQL environments and evolving data engineering toolchains. Test and troubleshoot cloud-based learning environments across AWS and Azure, validating services, permissions, storage, databases, compute resources, and streaming infrastructure. Investigate access and configuration issues involving federated cloud accounts, IAM/RBAC permissions, data access, and resource usage. Document changes clearly and provide step-by-step technical guidance that supports a consistent, high-quality learner experience. Requirements: At least 2 years of professional experience in a data-related role such as Data Analyst, Data Scientist, Data Engineer, or BI Developer. Strong knowledge of core data concepts, including data wrangling, exploratory data analysis, basic statistics, and data interpretation. Strong SQL skills, including the ability to write, debug, and optimize queries. Hands-on experience with Python for data work, including tools such as pandas, NumPy, scikit-learn, and data visualization libraries; experience with R is also valuable. Experience creating or interpreting data visualizations and dashboards, with familiarity with BI tools such as Power BI or Tableau. Familiarity with foundational data services and architectures on AWS or Azure, including storage, compute, data warehouses, and data lake patterns. Working knowledge of Docker, Jupyter notebooks, and notebook-based data workflows. Experience using version control systems such as Git or GitHub. Ability to debug and update Python- and SQL-based exercises, projects, and technical environments. Strong troubleshooting and problem-solving skills, particularly when resolving package conflicts, environment mismatches, SQL errors, visualization issues, and other technical problems. Excellent written communication skills, with the ability to document technical changes and create clear, step-by-step instructions. Ability to produce high-quality instructional and technical content aligned with professional industry standards. Strong attention to detail and a learner-first mindset. Experience applying statistics or machine learning in production environments is a plus. Relevant cloud or data certifications, such as AWS Data/Analytics or Azure Data Engineer certifications, are a plus. Ability to work independently, manage assigned maintenance projects, and collaborate effectively with cross-functional teams. Benefits: Remote contract opportunity based in India. Flexible engagement through an on-call contractor pool, with projects assigned as maintenance needs arise. Opportunity to work on a broad range of data science, analytics, machine learning, data engineering, and cloud technologies. Exposure to modern learning environments, including Jupyter, SQL, VS Code, Docker, AWS, and Azure. Clear project scopes, timelines, tooling, guidance, and logistical support for assigned engagements. Opportunity to contribute to high-quality technical education and improve learning experiences for students working with real-world data technologies. Compensation varies based on factors such as location, skills, experience, and the scope of the engagement.