Collaborate with teams to design, build, deploy, and monitor services while focusing on observability and proactive anomaly detection. Mitigate production issues using automation and scripts, and contribute to root cause analysis to prevent future incidents.
You will partner with stakeholders to solve complex business challenges by transforming ambiguous questions into structured analytical approaches and actionable recommendations. Additionally, you will design and maintain scalable analytical solutions, including dashboards and semantic models, while leveraging AI tools to enhance productivity and analytical accuracy.
The analyst will partner with stakeholders to provide actionable insights through data analysis and build intuitive Power BI dashboards. They will also utilize SQL and AI tools to query large datasets, identify trends, and support strategic business decisions.
Design, model, and maintain end-to-end BI solutions using Power BI, SQL, and Python to drive business decision-making. Collaborate with international teams to transform analytical findings into compelling narratives that connect data insights with business strategies.
Own the product direction for platform capabilities supporting the end-to-end machine learning lifecycle from data readiness to observability. Define the ML platform strategy and drive the adoption of standardized workflows and tools across data science teams.
Develop scalable analytical solutions and dashboards using Power BI, SQL, and Python to drive strategic decisions for Fintech teams. Partner with Product and Technology teams to transform complex data into actionable insights while ensuring data quality and governance.
Lead day-to-day support operations and incident management while ensuring adherence to SLAs and quality standards. Act as the primary liaison between Customer, Product, Engineering, and Operations teams to drive operational excellence.
Design and deploy machine learning models and optimization solutions to improve sales force effectiveness and customer engagement globally. Collaborate with cross-functional teams to translate commercial problems into scalable data science pipelines and production-grade code.
Design and deploy machine learning models and optimization solutions to improve global logistics delivery operations. Collaborate with cross-functional teams to translate real-world constraints into scalable mathematical models and production-grade code.
The role involves leading the development and maintenance of data services and solutions to support products, downstream services, or infrastructure tools used across BEES. This includes designing efficient data models and implementing architectural improvements to enhance performance, monitoring, and evolution of data products.
Implement ETL/ELT solutions and maintain data pipelines for large volumes of data. Collaborate with teams to ensure data security and optimize data processing systems.
