Manager - Data Analytics, Remittance and Fintech Business Development

Job Description

Requirements

• 4-year degree in Business Mathematics, Statistics, Data Science or related field.
• Master's in Business Administration is advantageous.
• Minimum 5 years experience in financial services, consulting, strategy, analytics, engineering, or related industry.
• 2 to 3 years experience in data structuration, data management, analytics, and big data.
• Experience working in an Agile or DevOps environment.
• Working knowledge in Hadoop, Apache Spark and related Big Data technologies (MapReduce, PIG, HIVE)
• Demonstrated experience with SAS, Tableau, Power BI, D3, VBA, SQL, and Business Objects.
• Highly proficient in database management systems like Postgres, Oracle, Mongo, MSSQL.

Responsibilities

• Support Fintech Analytics strategy execution in line with overarching business and functional goals.
• Provide reports and analysis to support development of functional strategy.
• Support execution and development of data mining and analysis frameworks.
• Ensure accurate and timely reporting to enable regular review of functional strategy, roadmap, and performance.
• Develop advanced quantitative modules using a variety of programs and software to support predictive assessment.
• Develop automated processes for data ingestion, transformation, and visualisation.
• Collate business intelligence data through public information, industry and field reports, or purchased sources.
• Develop predictive models to forecast remittance volumes, transaction patterns, and currency exchange rates.
• Monitor transaction data to ensure compliance with regulatory requirements.
• Drive analytics and reporting roadmap for business, prioritizing business insight requests to balance tactical and strategic information needs.
• Develop and manage models to ensure business and customer growth, including machine learning and data sciences components.
• Develop BI content including reports, dashboards, scorecards, and analytic solutions supporting effective business decisions.
• Perform complex analyses including optimization, text analytics, machine learning, social-science modeling, and statistical analysis.
• Support implementation and use of machine learning techniques to enhance fraud detection and risk management strategies.