Summary of Role: Work back from the business problems to be solved, collect proper data to do statistical analysis, select proper machine learning and/or deep learning modeling approaches, eventually rollout ML models in production environment to perfect business decision.
Meanwhile, coach junior associates during project collaborations.
Responsibilities:
• Understand business needs and explore appropriate data sources - be curious and proactive in exploring and understanding data.
• Perform data aggreagation, and feature engineering needed; write Python programming code to make visualizations, build, validate, and implement models.
• Collaborate with other data scientists and engineers.
• Be flexible and open to innovative ideas and alternative ways of solving problems.
• Be able to clearly communicate with and present the results to non-tech partners.
Experience:
• Master's degree (or higher) in Statistics, Data Science, Mathematics, Economics or related analytical discipline.
• At least one years’ experience in building end-to-end models in python (or similar language) through production. This requirement can be omitted for Ph.D. degree holders.
Skills:
• Proficiency in SQL and Python programming languages (pandas, numpy, scipy, scikit-learn, etc.)
• In-depth understanding of statistical knowledge and machine learning algorithms. Exposure and some deep learning knowledge are required.
• Specifically, expertise with the following techniques are must-haves to perform daily work: Linear Regression and GLM, GBM, Random Forest, XGboost, segmentation techniques, etc. Knowledge on Large Lanuage Models and Neural Networks are nice to have.
• Effective communication skills.
• Ability to learn new skills and independently take on tasks.
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