Data Scientist

Job Purpose

• Supervise the setup and application of analytical tools to generate insights for customer journeys and product enhancements by consolidating and analyzing large-scale, unstructured Big Data sources.
• Apply programming methods, processes, and systems to deliver data-driven insights.
• Proactively experiment with emerging technologies and tools to deliver innovative insights at high speed.
• Stay current with technical advancements and industry trends to continuously improve analytics capabilities.

Key Accountabilities

1. Data Solutioning

• Evaluate the effectiveness of analytical and predictive models and track business performance against model outputs.
• Design and build advanced algorithms using machine learning and deep learning techniques to deliver analytics solutions such as recommendation engines, customized models, customer journey analytics, graph models, etc.
• Drive adoption of machine learning and big data techniques across multiple journeys and squads.
• Manage, execute, and review complex data science projects in an Agile environment, ensuring compliance with internal regulatory requirements.

2. Data Insighting

• Lead the identification, interpretation, and communication of meaningful and actionable insights from large-scale data and metadata sources.
• Review and enhance processes and tools used to monitor model performance, data quality, and accuracy.
• Proactively lead data discussions across 3+ squads to identify analytical questions, hypotheses, and business issues.
• Collaborate closely with Data Engineers to design and implement complex analytical algorithms within data analytics platforms to improve efficiency and scalability.

3. Project Management

• Manage project conflicts, challenges, and changing business requirements to ensure high-performance delivery.
• Work with team leads to resolve people-related issues and project roadblocks.
• Conduct post-mortems and root cause analyses to help squads continuously improve delivery practices and productivity.

4. Talent Development

• Mentor and coach Data Analysts to develop into fully competent Data Scientists.
• Identify skill gaps and growth opportunities within the tribe and actively support capability development.

Key Relationships

Direct Reports

• Data Analysts
• Data Scientists

Success Profile

Qualifications

• Bachelor’s or Master’s degree in Statistics, Mathematics, Quantitative Analysis, Computer Science, Software Engineering, or Information Technology.

Experience

• Minimum 7+ years of experience in data analysis, machine learning, and deep learning model development on large-scale datasets.
• Strong experience in deploying statistical and machine learning models into production environments.
• Proficient in database querying and programming languages/tools such as C, C++, Python, R, Scala, SQL, Java, Tableau.
• Extensive experience in data mining, statistical analysis, data visualization, and advanced analytics solution development.
• Hands-on experience applying machine learning and AI to financial markets or financial services use cases.
• Proven ability to deliver fact-based insights that support senior management decision-making and enterprise-scale value creation.
• Deep understanding and hands-on experience with Agile Software Development; mastery of Agile principles, practices, and Scrum methodologies.
• Proven experience working in Agile teams and leading digital transformation initiatives from planning through implementation.
• English proficiency in accordance with Techcombank’s policy.