AI Scientist/Engineer

• Work on large-scale structured and unstructured data sets to solve a wide array of challenging problems using analytical, statistical, machine learning or deep learning approaches.
• Work with stakeholders from different departments to understand their business needs and challenges, architect and design analytics solutions to meet business objectives and support business decision making.
• Collaborate with cross-functional stakeholders to provide strategies based on data-driven insights across product, marketing, compliance, and others.
• Define, understand, and test external/internal opportunities to improve our products and services.
• Identify and measure success of product efforts through goal setting, forecasting, and monitoring of key product metrics.
• Develop data models, data automation systems, performance metrics, and reporting systems, and track impact over time.
• Deliver Machine Learning projects from beginning to end. This includes understanding the business need, planning the project, aggregating & exploring data, building & validating models, and deploying ML models to deliver business impact.
• Improving upon existing machine learning methodologies by developing new sources, developing and testing enhancements, running computational experiments, and fine-tuning parameters.
• Communicate results and business impacts of insight initiatives to stakeholders within and outside of the company.

• Bachelor degree in Business, Economics, Statistics, Data Science, Data Mining, or similar quantitative field.
• Proven successful and trackable experience in an analytical role or data scientist role involving extraction, analysis, and/or modeling.
• Solid experiences in SQL, familiar with SQL functions such as window functions and aggregate functions.
• Solid experiences in Python, familiar with data analysis libraries such as pandas, numpy, matplotlib, scikit-learn, etc.
• Experience in using analytical concepts and statistical techniques: hypothesis development, designing tests/experiments, analysing data, drawing conclusions, and developing actionable recommendations for business units.
• Experience in developing production-grade ML systems including exploratory analysis, feature engineering, hyperparameter tuning, model training, model selection, creating data pipelines, etc.
• Experience in working with deep learning frameworks such as PyTorch for real world problems.
• Knowledge of Computer Science fundamentals such as object-oriented design, graph algorithm, algorithm design, data structures, problem solving and complexity analysis.
• Self-driven, innovative, collaborative, with good communication and presentation skills, able to translate between business and technical audiences.

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