As a Data Science Analyst, you will help deliver and implement analytics-driven strategies and high-impact solutions for clients in a consulting and payments environment. You will use your data science, analytics, and banking or payments expertise to solve complex problems, unlock business value, and turn large-scale data into meaningful client outcomes.
What You’ll Do and How You’ll Succeed
- Analyse data using statistical software such as Python and R to generate insights that support client performance and profitability.
- Extract, aggregate, and work with large datasets using SQL, Hive, Spark, and related tools to support robust analytics delivery.
- Deploy and support predictive modelling packages, while monitoring and calibrating production models to maintain performance and relevance.
- Apply innovative data science techniques to solve complex business problems and uncover opportunities for value creation.
- Collaborate with data science colleagues and cross-functional client teams to deliver clear, relevant, and actionable insights to clients.
- Automate analyses and build repeatable or self-service tools that improve efficiency and scalability.
- Ensure timely project delivery in line with business needs and stakeholder expectations.
- Promote data innovation and analytics education within the company to strengthen capability and adoption.
- Optimise code that supports critical business processes to improve performance and reliability.
- Design and develop dashboards using tools such as Tableau or Power BI to support data-driven decision-making.
- Communicate complex concepts clearly and effectively to both technical and non-technical stakeholders.
We’d Love to Hear From You If…
Experience
- You have 4+ years of professional work experience, preferably in banking, payments, fintech, or a related industry.
- You have a degree in a quantitative field such as Statistics, Mathematics, Physics, Computer Science, Economics, or Engineering, or equivalent experience.
- A Masters or Ph.D. will be an advantage.
- You have demonstrated experience planning, organising, and managing multiple concurrent analytics projects across diverse cross-functional stakeholders.
- A consulting background, whether from a consulting firm or through similar experience orchestrating complex solutions across stakeholders and current-state environments, will be an advantage.
Technical Expertise
- You have hands-on experience with data analytics and programming tools such as Python and R.
- You have advanced querying skills using SQL or other query languages.
- You have hands-on experience working in big data ecosystems such as Hadoop, Spark, Hive, Kafka, GCP, AWS, and Azure.
- Experience working across both on-prem and cloud environments will be a plus.
- You have hands-on experience with BI tools such as Power BI and Tableau.
- You are proficient in statistical techniques including Neural Networks, Gradient Boosting, Linear and Logistic Regression, Decision Trees, Random Forests, Markov Chains, Support Vector Machines, Clustering, Principal Component Analysis, and Factor Analysis.
- You have strong Excel and PowerPoint skills, including storyboarding and data storytelling.
- A strong understanding of the payments and or banking industry, including consumer credit, consumer debit, small business, commercial, co-brand, and merchant, will be an advantage.
- Strong commercial acumen, including experience working on business or value cases that demonstrate ROI, will be an advantage.
Ways of Working
- You are motivated, analytical, and curious about the payments industry and the application of data science.
- You have a track record of using data science and analytics expertise to unlock business value.
- You are results-driven and client-centric in how you approach problem-solving and delivery.
- You can manage internal and external stakeholders with a collaborative, diplomatic, and flexible style.
- You work effectively in a matrixed organisation.
- You bring excellent presentation, storytelling, oral, and written communication skills.
Assignment Details
- Location: Indonesia