Job
Description
Zeta is a Next-Gen Banking Tech company that empowers banks and fintechs to launch banking products for the future. It was founded by and Ramki Gaddipati in 2015. Our flagship processing platform - Zeta Tachyon - is the industry's first modern, cloud-native, and fully API-enabled stack that brings together issuance, processing, lending, core banking, fraud & risk, and many more capabilities as a single-vendor stack. 15M+ cards have been issued on our platform globally. Zeta is actively working with the largest Banks and Fintechs in multiple global markets transforming customer experience for multi-million card portfolios. Zeta has over 1700+ employees - with over 70% roles in R&D - across locations in the US , EMEA , and Asia . We raised $280 million at a $1.5 billion valuation from Softbank, Mastercard, and other investors in 2021. The Role The Lead data Scientist will be responsible for organizing and reporting data related to sales numbers, market research, logistics, linguistics, or other behaviors. They utilize technical expertise to ensure data reported is accurate and high-quality. Data will need to be analysed, designed, and presented in a way that assists individuals, business owners and customer stakeholders to make better decisions. Responsibilities: Cross-Functional Collaboration: Collaborate seamlessly with Engineering, Product, and Operations teams to conceptualise, design, and construct data reporting and analytical systems. Ideation and Analysis: Generate ideas for exploratory analysis, actively shaping the trajectory of future projects. Provide insightful recommendations for strategic actions based on data-driven insights. Rapid Prototyping and Product Discussions: Drive the rapid prototyping of solutions, actively participating in discussions related to product and feature development. Dashboard Creation and Reporting: Develop dashboards and comprehensive documentation to effectively communicate results. Regularly monitor key data metrics, facilitating informed decision-making. Integration with Production Systems: Collaborate closely with software engineers to deploy and integrate data models into production systems. Ensure scalability, reliability, and efficiency of integrated solutions. Business Metrics Identification: Identify and analyze key business metrics, offering strategic insights. Recommend product features based on the identified metrics to enhance overall product functionality. Understand and manage Data Infrastructure: Lead the design and development of scalable, robust, and efficient data architectures. Oversee the development and maintenance of ETL (Extract, Transform, Load) processes to move and transform data from various sources into data warehouses or other storage solutions. Ensure data quality and integrity throughout the data pipeline. Team Leadership: Lead a team of data engineers and data analysts, providing mentorship, guidance, and technical expertise. Coach and manage a team to deliver using Agile processes and ensure high RoI.Participating actively in recruitment and nurturing of engineers as awesome as you. Skills Exceptional quantitative and problem-solving skills - capable of tackling complex data-driven challenges and formulating effective solutions. Proven proficiency - In essential data science libraries, including Pandas, Numpy, SciPy, and Scikit-Learn, for data manipulation, analysis, and modeling. In-depth expertise in Python programming and SQL - Focus on data analysis, model building, and algorithmic implementation. Experience with distributed computing frameworks - Hadoop or Spark, for handling large-scale data processing and machine learning tasks. Data Architecture - Designing and implementing robust data architectures that support the organization's needs. This includes understanding different database systems, data lakes, and data warehouses.
