Job Description
Responsibilities
- · Work with RURA’s internal teams and external partners to define and plan data analysis activities aligned with regulatory goals.
- · Conduct descriptive and exploratory analysis on large and complex datasets to uncover trends, patterns, and insights relevant to regulation and service delivery.
- · Perform data landscaping across government and regulated sectors to identify available datasets, assess their quality, and evaluate potential for analysis.
- · Evaluate data quality and completeness, and recommend techniques or standards to improve accuracy, consistency, and usability.
- · Merge and harmonize datasets from multiple sources to enable comprehensive, multi-dimensional analysis.
- · Create visualizations, dashboards, and summary reports to communicate analytical findings clearly to both technical and non-technical stakeholders.
- · Maintain clear documentation of data sources, analysis methods, and results, ensuring transparency and reproducibility of work.
- · Ensure compliance with Rwanda’s legal and regulatory frameworks for data use, including personal data protection and confidentiality requirements.
Requirements
- A Master’s degree in data science, statistics, applied mathematics or computer science.
- Minimum of a Bachelor’s degree in data science, statistics, applied mathematics or computer science with at least 3 years of relevant experience.
- · A post-graduate degree in data science, statistics, applied mathematics, computer science, or a related field.
- · One to three years of experience working on data-centric projects, preferably in research, public policy, or regulatory environments.
- · Demonstrated ability to extract, clean, and transform complex datasets, and collaborate with data owners to understand data structures and limitations.
- · Proficient in working with both OLAP and OLTP databases such as Clickhouse, MySQL, PostgreSQL, or similar.
- · Willingness and ability to work in Linux-based server environments, including basic scripting and data processing from the command line.
- · Strong programming skills in R or Python (or a comparable language), especially for data manipulation, visualization, and statistical analysis.
- · Experience with data visualization tools (e.g., Power BI, Tableau, or ggplot/seaborn/matplotlib) is an asset.
- · Strong analytical thinking, communication skills, and the ability to translate data insights into actionable recommendations for diverse audiences.
- · Knowledge of data privacy, ethical data use, and Rwanda’s legal frameworks on personal data protection is a plus.
- · Data Analysis & Interpretation: Ability to explore, analyze, and derive insights from structured and unstructured datasets using statistical methods.
- · Programming Skills: Proficient in R or Python for data cleaning, transformation, analysis, and visualization.
- · Database Querying: Skilled in writing SQL queries to extract and manipulate data from relational databases (e.g., Clickhouse, PostgreSQL, MySQL).
- · Data Visualization: Experience with creating clear and compelling data visualizations using tools like Power BI, Tableau, matplotlib, seaborn, or ggplot2.
- · Statistical Literacy: Solid understanding of statistical techniques and concepts (e.g., distributions, confidence intervals, hypothesis testing).
- · Data Wrangling: Skilled at handling real-world data issues such as missing values, inconsistent formats, and messy text fields.
- · Communication: Ability to present findings clearly to technical and non-technical stakeholders through written reports, dashboards, and presentations.
- · Documentation: Comfortable documenting methodologies, data sources, and analytical processes to ensure transparency and reproducibility.
- · Problem-Solving: Strong analytical thinking and ability to develop clear problem statements and data-driven approaches to complex challenges.
- · Data Governance Awareness: Understanding of data quality principles, data protection regulations, and ethical data use in the Rwandan context.
- · Collaboration: Experience working across teams and institutions, with a willingness to learn from and support colleagues.
- · Self-Learning: Curious and proactive about learning new tools, methods, and sector-specific applications of data.
- · Possesses strong problem-solving skills and attention to detail.
- · Works well independently and collaboratively.
- · Has a solid grounding in statistics and probability.
- · Can work with both structured and unstructured data sources.
- · Is capable of framing analytical problems, building data-driven solutions, and communicating insights clearly.
- · Brings a creative and enthusiastic approach to uncovering insights that address real-world or economic challenges.
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