About the Role:
The Data Science Analyst is responsible for delivering analytics-driven insights and data science solutions to support strategic business decision-making. This role involves analyzing large-scale datasets, developing predictive models, and collaborating with cross-functional stakeholders to unlock business value through advanced analytics.
You will work closely with data scientists, analysts, and business teams to design scalable analytics solutions, automate processes, and translate complex data into actionable insights that drive performance and profitability.
Contract Duration: 12 months, with the possibility of extension.
Job Descriptions:
- Analyze large datasets using statistical and programming tools such as Python and R.
- Extract, transform, and aggregate data from multiple sources using SQL, Hive, Spark, and other data processing tools.
- Develop, deploy, and maintain predictive models, including monitoring and calibration in production environments.
- Apply advanced data science techniques to solve complex business problems and generate insights.
- Collaborate with cross-functional teams to deliver analytics-driven recommendations.
- Automate repeatable analyses and develop self-service analytics tools.
- Ensure timely delivery of analytics projects aligned with business requirements.
- Optimize code and data pipelines for performance and scalability.
- Design and develop dashboards using BI tools such as Power BI or Tableau.
- Communicate complex analytical concepts clearly to both technical and non-technical stakeholders.
- Promote data-driven decision-making and analytics best practices across teams.
Requirements:
- Bachelor's degree in a quantitative field such as Statistics, Mathematics, Computer Science, Economics, Engineering, or related discipline (Master's or PhD is an advantage).
- Minimum 4 years of experience in data science, analytics, or related roles, preferably within banking, payments, fintech, or similar industries.
- Strong programming skills in Python or R, with advanced SQL querying capabilities.
- Hands-on experience working with Big Data technologies such as Hadoop, Spark, Hive, Kafka, GCP, AWS, or Azure.
- Experience using BI and visualization tools such as Power BI or Tableau.
- Strong knowledge of statistical and machine learning techniques, including regression, decision trees, random forests, clustering, PCA, and other predictive modeling approaches.
- Proven ability to manage multiple analytics projects and work with cross-functional stakeholders.
- Excellent communication, presentation, and data storytelling skills.
- Strong analytical thinking and problem-solving capabilities.
- Proficiency in Excel and PowerPoint for reporting and storytelling.
- Experience in consulting or advisory-driven analytics environments.
- Understanding of the banking, payments, or financial services industry.
- Strong commercial acumen with experience developing business cases and ROI analysis.
