Data Scientist

Job Summary

We are looking for a highly analytical and business-savvy Data Scientist to join our global business intelligence & strategy team. The ideal candidate is a data master who can translate complex datasets into clear, actionable business strategies, and build machine learning models that turn data into predictive, automated decision-making. You will be responsible for detecting business abnormalities, performing deep-dive root cause analysis, developing predictive and analytical models, and delivering high-level summaries that guide our internal leaders and global clients.

Key Responsibilities

·       Business Intelligence& Analysis: Deep-dive into customer behavior, business performance, and operational metrics to identify growth opportunities and potential risks.

·       Machine Learning &Modeling: Design, build, train, and deploy machine learning models (e.g., forecasting, classification, anomaly detection, customer segmentation, churn/risk prediction) to drive predictive insights and automate decision-making at scale.

·       Root Cause Investigation: Proactively identify data abnormalities (e.g., performancedips, operational shifts) and perform comprehensive root cause analysis toprovide clarity to stakeholders.

·       Technical Execution: Develop and optimize automated data pipelines and dashboards using SQL and Python to ensure high-quality, real-time reporting, and operationalize models into production-ready workflows.

·       Synthesis &Communication: Translate complex technical findings and model outputs into concise executive summaries and natural language insights for non-technical business leaders.

·       Strategic Collaboration: Act as a bridge between data engineering and business units to ensure analytical and modeling solutions are perfectly aligned with real-world business needs.

Qualifications

·       Educational Background: Bachelor's or Master's degree in a quantitative field (e.g.,Computer Science, Statistics, Data Science, Machine Learning, or Finance).

·       Technical Mastery: Proven ability to query large, complex databases and optimize performance using SQL.

·       Proficient Python: Strong coding skills for data manipulation (Pandas, NumPy) and automated analysis.

·       Machine Learning Expertise: Hands-on experience building and deploying ML models using frameworks such as scikit-learn, XGBoost, TensorFlow, or PyTorch, including feature engineering, model evaluation, and tuning. Familiarity with MLOps practices (model deployment, monitoring, and versioning)is a plus.

·       Business Analysis Experience: 5+ years of experience in data analysis or data science, specifically in identifying business trends and solving real-world problems.

·       Synthesizing Ability: Exceptional ability to "tell a story" with data, moving beyond the "what" to explain the "why" and "so what" in clear business terms.

·       Industry Context: Experience in Fintech (Crypto, Trading, Banking) or relatedfinancial data environments is highly desirable.