Project Manager, Data Analytics and Engineering

Department: Product Management

Experience: 2 years + Masters

• Understand and address client data requirements through targeted data analysis while enforcing documented data governance and security protocols.
• Collate, interpret, and analyze complex data sets using advanced machine learning and artificial intelligence software and manage orchestration of high-volume data pipelines (e.g., Apache Airflow) that can ingest and process data sets containing at least 30 million records monthly into enterprise data-warehousing environments.
• Focus on data exploration and report generation, using visualization tools for effective client- side data analysis.
• Write, analyze, and present query results, preparing comprehensive reports from analyzed data.

• Lead advanced statistical analysis and modeling by overseeing application of advanced modeling and statistical techniques such as logistic regression, classification and regression trees, neural nets, clustering, survival analysis, and segmentation.
• Lead development and comparison of predictive and descriptive quantitative models using mathematical techniques and validate model performance against client KPIs in partnership with engineering teams.

• Act as a liaison between client and internal data teams, resolving issues and ensuring effective communication on gathering requirements, defining scope of work, managing system integrations, thirdparty data connections and scope changes.
• Define and manage work plans and action steps, ensuring project alignment with objectives while tracking budgets, resource allocation and deliverable schedules from planning through delivery.
• Provide daily oversight of offshore technical teams responsible for data aggregation,warehousing and large‑scale batch processing to maintain SLAs for data freshness and accuracy.

• Master’s degree in Business Analytics, Data Analytics, Mathematics, Statistics, or Computer Science and 24 months (2 years) of experience in any occupations in which required experiences were gained (may be pre-Master’s). Required experience:
• 2 years of experience performing data modeling, including logistic regression, classification and regression trees, neural nets, clustering, survival analysis, and segmentation.
• 2 years of experience managing data analytics projects from planning through delivery, including developing project schedules and tracking deliverables; overseeing data pipeline and warehouse implementations; managing system integrations and third-party data connections; facilitating requirements gathering and scope definition; managing project budgets and resource allocation; and interfacing with business and engineering teams.
• 2 years of experience working on the orchestration of data pipelines, including scheduling, monitoring, and managing high volume data workflows (at least 10 million records monthly) using tools like Apache Airflow or similar tools.
• Experience managing data aggregation, warehousing, and processing of at least 10 million records monthly.
• Experience managing compliance with data governance and security protocols.