Description:
We are seeking a highly skilled Data Analyst with a strong background in statistics and experience handling large datasets and big data environments. The successful candidate will be responsible for analyzing complex datasets, identifying trends, and generating actionable insights. They will use advanced statistical methods and data visualization techniques to create infographics and reports that clearly communicate findings to both technical and non-technical stakeholders.
Key Responsibilities
- Big Data Analysis: Work with massive datasets from various sources (structured and unstructured) in big data environments, such as Hadoop, Spark, or NoSQL databases. Analyze data to uncover trends, patterns, and insights that drive business decisions.
- Statistical Analysis: Apply advanced statistical techniques (e.g., regression analysis, hypothesis testing, time-series forecasting, etc.) to interpret data, validate results, and develop predictive models that support decision-making. Provide statistical support for A/B testing and experimental design.
- Data Wrangling & Processing: Clean, preprocess, and transform large-scale datasets for analysis, ensuring accuracy and consistency. Implement data pipelines and ETL processes to manage and aggregate data from different sources using tools like SQL, Python, or R.
- Visualization & Infographics: Create compelling data visualizations, dashboards, and infographics to summarize key insights, using tools like Ploty, Tableau, Power BI, or D3.js. Ensure data-driven stories are visually engaging and accessible to both technical and non-technical audiences.
- Data Reporting & Insights: Deliver detailed reports and presentations to stakeholders, highlighting key findings from data analysis. Translate complex data into understandable insights to guide business strategy, marketing efforts, and operational improvements.
- Collaboration with Data Engineering: Work closely with data engineering teams to ensure that the infrastructure for data collection, storage, and analysis is optimized for scale and efficiency. Collaborate on the design of scalable data models that support deep analytics.
- Big Data Tooling: Leverage big data processing frameworks such as Apache Spark, Hadoop, and Kafka to manage and analyze large datasets efficiently. Utilize cloud-based data solutions (e.g., AWS, Azure, or Google Cloud) for scalable storage and compute resources.
- Collaboration with HQ: Work closely with global teams, ensuring alignment of data practices and insights across the organization, leveraging being in EMEA timezone. Provide data-driven recommendations to influence strategic decision-making at the regional and global levels.
Requirements
- 3+ years of experience in data analysis, with strong expertise in statistics, big data, and data visualization.
- Proficiency in handling large datasets using tools like SQL, Python, R, and big data platforms such as Hadoop or Apache Spark.
- Strong knowledge of statistical analysis methods (e.g., hypothesis testing, regression models, predictive modeling) and experience applying them in real-world datasets.
- Expertise in creating data visualizations and infographics using tools such as Tableau, Power BI, or D3.js.
- Hands-on experience with ETL processes, data wrangling, and building scalable data pipelines.
- Hands-on experience doing exploratory data analysis with python.
- Strong communication skills with the ability to explain technical results to non-technical stakeholders.
- Experience in working with cloud-based data ecosystems (e.g., AWS, Google Cloud, Azure).
- References to a particular number of years experience are for indicative purposes only. Applications from candidates with equivalent experience will be considered, provided that the candidate can demonstrate an ability to fulfill the principal duties of the role and possesses the required competencies.