Data Scientist

<p><strong>About Us</strong></p> <p>Hytech is a leading management consulting firm headquartered in Australia and Singapore, specializing in digital transformation for fintech and financial services companies. We provide comprehensive consulting solutions, as well as middle- and back-office support, to empower our clients with streamlined operations and cutting-edge strategies.</p> <p><br></p> <p>With a global team of over 2,000 professionals, Hytech has established a strong presence worldwide, with offices in Australia, Singapore, Malaysia, Taiwan, Philippines, Thailand, Morocco, Cyprus, and more.</p> <p>&nbsp;</p> <p><strong>Job Summary</strong></p> <p>We are looking for a highly analytical and business-savvy Data Scientist to join our data platform &amp; intelligence 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.</p> <p><br></p> <p><strong>Key Responsibilities</strong></p> <ul> <li><strong>Business Intelligence &amp; Analysis:</strong> Deep-dive into customer behavior, business performance, and operational metrics to identify growth opportunities and potential risks.</li> <li><strong>Machine Learning &amp; Modeling:</strong> 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.</li> <li><strong>Root Cause Investigation:</strong> Proactively identify data abnormalities (e.g., performance dips, operational shifts) and perform comprehensive root cause analysis to provide clarity to stakeholders.</li> <li><strong>Technical Execution:</strong> 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.</li> <li><strong>Synthesis &amp; Communication:</strong> Translate complex technical findings and model outputs into concise executive summaries and natural language insights for non-technical business leaders.</li> <li><strong>Strategic Collaboration:</strong> Act as a bridge between data engineering and business units to ensure analytical and modeling solutions are perfectly aligned with real-world business needs.</li> </ul> <p><br></p> <p><strong>Qualifications</strong></p> <ul> <li><strong>Educational Background:</strong> Bachelor's or Master's degree in a quantitative field (e.g., Computer Science, Statistics, Data Science, Machine Learning, or Finance).</li> <li><strong>Technical Mastery:</strong> Proven ability to query large, complex databases and optimize performance using SQL.</li> <li><strong>Proficient Python:</strong> Strong coding skills for data manipulation (Pandas, NumPy) and automated analysis.</li> <li><strong>Machine Learning Expertise:</strong> 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.</li> <li><strong>Business Analysis Experience:</strong> 5+ years of experience in data analysis or data science, specifically in identifying business trends and solving real-world problems.</li> <li><strong>Synthesizing Ability:</strong> Exceptional ability to "tell a story" with data, moving beyond the "what" to explain the "why" and "so what" in clear business terms.</li> <li><strong>Communication:</strong> Strong communication skills, with fluency in English and Mandarin, to effectively collaborate with diverse, cross-functional teams.</li> <li><strong>Industry Context:</strong> Experience in Fintech (Crypto, Trading, Banking) or related financial data environments is highly desirable.</li> </ul>