Senior Data Scientist

Key Responsibilities:

• Data Quality Analysis & Remediation: Collect, extract, and clean data from various sources to prepare comprehensive datasets for analysis This includes handling missing data, removing inconsistencies, and ensuring the data is structured correctly for further analysis.
• Analyze data using appropriate statistical techniques and tools to uncover trends, patterns, and anomalies in the information. This could involve anything from simple descriptive analysis to more complex exploratory data analysis
• Validate and cross-check analysis results to ensure accuracy. This involves verifying calculations, comparing findings against external or historical data for consistency, and investigating any irregularities in the results. Data Analysts are expected to be vigilant about data quality during analysis, catching any issues that might have passed initial data processing.
• Create clear, concise reports and visualizations to communicate findings. While the focus is on analysis rather than just creating dashboards, analysts will use tools like Power BI to illustrate key insights. They should present data in an understandable way for internal stakeholders, writing summaries that interpret the numbers and highlight important conclusions.
• Work closely with statisticians and other team members to interpret results and refine analysis approaches. This collaborative approach ensures that the analysis aligns with methodological standards and project goals. Analysts may adjust their methods based on feedback, contribute to discussions on what the data means, and help integrate their findings into broader census reports.

Key Qualifications:

• Bachelor’s degree in a quantitative field such as Statistics, Data Science, Economics, Mathematics, or similar. This educational background provides a solid foundation in data analysis techniques and statistical reasoning.
• Strong proficiency in data analysis tools and languages, particularly SQL for database querying, Excel for data manipulation, and programming in Python or R for more advanced analysis and automation. Experience with statistical libraries or packages (e.g., pandas, R’s tidyverse) is highly valuable.
• Demonstrated ability to perform in-depth data analysis and not just generate visuals. We need analysts who can write complex queries, perform calculations, and apply statistical tests if necessary – beyond assembling dashboard components. An analytical mindset and attention to detail are crucial for interpreting data correctly and spotting outliers or errors.
• Familiarity with business intelligence and visualization tools, especially Power BI, to aid in presenting analysis outcomes. While creating polished dashboards is not the primary focus, the analyst should be capable of using Power BI (and similar tools like Tableau if needed) to turn data into interpretable charts and graphs for reporting purposes. They should also understand data visualization best practices to communicate information effectively.
• Excellent communication skills, both written and verbal, with the ability to explain data findings in layman’s terms. A collaborative attitude is important, as Data Analysts will frequently interact with other teams (IT, field operations, subject-matter experts) to gather context and ensure that analyses meet the needs of the census project. They should be proactive in sharing insights and contributing to data-driven decisions within the organization.