Search by job, company or skills

Data Scientist

Data Scientist

MetaComp
3-5 Years
Not Disclosed

This job is no longer accepting applications

Job Description

About Us

MetaComp Pte Ltd is a leading Singapore-based digital payment solution provider, licensed and regulated by the Monetary Authority of Singapore (MAS) as a Major Payment Institution, to provide Digital Payment Token Services and Cross-border Payment Transfers. Operating under a P2B2C (platform-to-business, partners-to-clients) model, MetaComp provides its clients with an integrated end-to-end suite of services, empowering them to confidently enter the digital asset market with much-needed safety, security, and compliance assurance. Together with its parent company, Metaverse Green Exchange Pte. Ltd. (a MAS-licensed CMS holder permitted to carry out, inter alia, brokerage and custody services), MetaComp introduces its suite of services through CAMP (Client Assets Management Platform) which allow businesses to develop and scale their digital asset offerings through various products and/or services such as over-the-counter transactions, fiat payments, digital asset custody and prime brokerage.

Role Overview

We are looking for a hands-on Data Scientist (Business Intelligence & Applied Analytics) to turn complex business and transaction data into trusted insights, dashboards, and analytical solutions. You will work across payments, digital assets, finance, operations, risk, and compliance, combining business intelligence with statistical and machine-learning techniques to support better decisions.

Key Responsibilities

  • Translate business questions into KPIs, dashboards, analytical models, and actionable recommendations.
  • Build and maintain BI dashboards for management, Finance, Product, Operations, Risk, and Compliance.
  • Define standardized metrics, semantic models, and reporting logic to ensure consistent business reporting.
  • Analyze customer, transaction, revenue, operational, and external data to identify trends, risks, and opportunities.
  • Develop forecasting, anomaly-detection, fraud, risk-scoring, and customer-segmentation models.
  • Design data marts and analytical datasets with Data Engineers.
  • Automate recurring reports and enable governed self-service analytics.
  • Validate data accuracy and reconcile BI outputs with operational and financial systems.
  • Monitor dashboard usage, data freshness, model performance, and reporting quality.
  • Present findings clearly to management and business stakeholders.
  • Maintain documentation for KPIs, reports, analytical methods, and data definitions.

Requirements

  • Bachelor's or Master's degree in Data Science, Statistics, Mathematics, Computer Science, Economics, or a related field.
  • At least 3 years of experience in data science, business intelligence, or advanced analytics.
  • Strong proficiency in SQL and Python.
  • Hands-on experience with Power BI, Tableau, Looker, Metabase, or similar BI platforms.
  • Experience building executive dashboards, operational reports, and reusable analytical datasets.
  • Good understanding of KPI design, dimensional modelling, semantic layers, and data visualization.
  • Solid knowledge of statistics, forecasting, experimentation, and machine learning.
  • Experience working with large structured and semi-structured datasets.
  • Ability to translate business requirements into measurable analytical solutions.
  • Strong business acumen, problem-solving skills, and stakeholder communication.
  • Professional proficiency in English and Mandarin for collaboration with Chinese-speaking vendors and technical teams.

Nice to Have

  • Experience in fintech, payments, banking, blockchain, or digital assets.
  • Knowledge of financial reconciliation, revenue analytics, AML, fraud, transaction monitoring, or regulatory reporting.
  • Experience with Power BI semantic models, DAX, data marts, and row-level security.
  • Experience with forecasting, anomaly detection, customer segmentation, or graph analytics.
  • Familiarity with Spark, Airflow, dbt, MLflow, or cloud data platforms.
  • Experience with generative AI, natural-language BI, or AI-assisted analytics.
  • Understanding of data governance, model governance, privacy, and regulated environments.

We are committed to creating an inclusive workplace where every individual feels respected, valued, and empowered to contribute. We celebrate diversity in all its forms—background, ethnicity, gender, identity, orientation, experience, and thought—and believe it strengthens our culture and our work. We are proud to be an equal opportunity employer and do not discriminate on the basis of race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, disability, or any other protected characteristic.

More Info

Job Type:
Industry:
Employment Type:

Key Skills

About Company

Similar Jobs

2-5 yrs
Malaysia, Kuala Lumpur
Skills:
Tableau, Excel, Python, Sql, R
2-5 yrs
Malaysia, Kuala Lumpur
Skills:
regression models , snowflake , Power Bi, Tableau, Redshift, Sql, Quicksight, Time Series Analysis, Decision Trees, Spark, Databricks, Azure, Python, AWS, Gradient Boosting Models, Clustering and Segmentation, Random Forests, Recommendation Systems, Customer Propensity Modeling
1-5 yrs
Malaysia, Kuala Lumpur
Skills:
Hadoop, Data Security, Data Quality, Data Governance, Clustering, Sql, Metadata Management, Spark, Predictive Modeling, Python, LLMs, Iceberg, Superset, Classification techniques, Knowledge graph
7-9 yrs
Malaysia, Kuala Lumpur
Skills:
snowflake , Machine Learning, Predictive Modelling, Sql, Google Cloud, Tensorflow, Pytorch, Python, AWS, Airflow, Agentic AI architectures, MLOps orchestration, Statistics, MLflow
5-8 yrs
Malaysia, Kuala Lumpur
Skills:
snowflake , Machine Learning, Predictive Analytics, Sql, Azure ML, MLops, Statistical Modelling, Databricks, Clustering, Python, Generative AI, model deployment, event-triggered workflows, recommendation engines, CI CD, conversational AI solutions, LLM applications, Segmentation, AI agents, Microsoft Fabric, real-time scoring, RAG architectures, model monitoring