Chuyên gia Khoa học Dữ liệu - MSB - DT - 1M470

Objectives:

• The job holder investigates business problems, extract, analyze and interpret large amounts of data from a range of sources, using advanced algorithmic, data mining, artificial intelligence, machine learning, deep learning, data visualization and statistical tools, in order to make it accessible to businesses and deliver pre-agreed specific business KPIs.
• The job holder initiates and supervises the setup of ML projects together with business to generate pre-agreed KPI-driven solutions using state-of-the-art DS methods, processes, systems and tools on structured & unstructured, diverse Big Data sources.
• The job holder is required to take initiative in experimenting various technologies and tools with vision of creating innovative data driven solutions for the business at the quickest pace possible and keep current with technical and industry developments.

Key Responsibilities:

• Mine, profile, clean and analyze data from company’s databases to drive optimization and improvement of product development, customer journey experiences, revenue uplift, marketing techniques and business strategies.
• Assess the effectiveness and accuracy of multiple data sources and data gathering techniques.
• Know at all times your data (size, average, distributions, outliers, etc.) and be able to estimate model output, impact and come up with sanity checks to detect bugs (discrepancies between expectations and results). Analyze data for trends and patterns and interpret the data with clear objective and outcome in mind.
• Translate business problems into data analytics ones, build algorithms and work with machine learning and deep learning tools to deliver advance analytics solutions across the firm including classification, regression, recommendation engines, clustering, time series forecasting, probabilistic & statistics modeling, etc.
• Collaborate with use case leads and campaign teams for end-to-end analytics use cases with baselining, modeling, stratified sampling, A/B testing design and go-to-market plan.
• Execute and review data science projects in an Agile manner and in compliance with internal regulatory requirements.
• Develop processes and tools to monitor and analyze model performance and prediction accuracy.
• Guarantee machine learning models are developed with modular, maintainable & scalable code bases.

As a senior data scientist, you will also:

• Evaluate effectiveness of proposed models and track business performance KPIs against data model.
• Drive application of machine learning and big data techniques across different journeys and squads.
• Manage, execute, and review complex data science projects in an agile manner and in compliance with internal regulatory requirements.
• Lead the identification and interpretation of meaningful and actionable insights from large data and metadata sources together with business partners.
• Review processes and tools designed to monitor and analyze model performance and prediction accuracy.
• Proactively lead discussions in 3+ squads to identify questions and issues for data science
• Collaborate with Data Engineers & ML Engineer to build complex, technical algorithms in data analytics software applications to improve work efficiency.
• Manage project conflicts, challenges and dynamic business requirements to keep operations running at high performance.
• Work with team leads to resolve people problems and project roadblocks, conduct post mortem and root cause analysis to help squads continuously improve their practices to ensure maximum productivity.
• Mentor and coach junior fellows into fully competent Data Scientists.
• Identify and encourage areas for growth and improvement within the team.

Key Relationship:

• Line Manager: Analytics Head, Head of Data & Advanced Analytics
• Internal: BI Head, Use Case Leads
• External: Digital Factory leaders, Business Divisions leaders

Qualification and Experiences:

• Master's degree (or higher) in Statistics, Mathematics, Quantitative Analysis, Computer Science, Software Engineering, Information Technology or other Numerical Disciplines. Ph.D. degree in the field is a plus.
• Experience in querying databases, coding and statistical computer languages (e.g. Python, Scala, Java, SQL)
• 4-8 years of relevant experience in areas of data analytics, model development on large amount of data, implementing and deploying various statistical, machine learning & deep learning models such as: Simulation, GLM/Regression, Classification, Decision Trees, Random Forest, Gradient Boosted Trees, Neural Networks, Text Mining, Graph Learning, Social Network Analysis, etc.
• Experience with data processing, statistics, machine learning and deep learning tools: Numpy, Pandas, Statsmodels, Scikit-Learn, Tensorflow, Keras, Pytorch, etc.
• Experience with distributed data/computing tools: HDFS, Hive, Presto, PySpark, etc.
• Experience with visualizing/presenting data for stakeholders using: D3.js, ggplot, matplotlib, seaborn, plotly, etc.
• Experience with cloud services (AWS, GCP, Azure)
• Experience in application of machine learning and AI to questions related to the financial sector
• Experience in providing fact-based insights to drive decision-making and help senior management and other stakeholders realize enterprise value at scale
• English proficiency requirements are pursuant to MSB's policy
• Experience with communicating complex analysis and models across a diverse team
• Excellent written and verbal communication skills for coordinating across teams
• Understanding of Agile principles, practices and Scrum methodologies
• Experience working in Agile teams to support digital transformation projects

• Degree: Postgraduate
• Age: Unlimited
• Working time: Permanent