AI Lead 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

More than 8 years of industrial experience, with deep knowledge of recommendation systems, with at least 3-year experience of team leading.

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.

Self-driven, innovative, collaborative, with good communication and presentation skills, able to translate between business and technical audiences.