Research and develop advanced machine learning methods to assess the technical feasibility and potential business value
Utilizes effective project planning techniques to break down basic and occasionally moderately complex projects into tasks and ensure deadlines are kept
Uses and learns a wide variety of tools and languages to achieve results (e.g., Python, SQL, machine learning libraries)
Collaborates with the team in order to improve the effectiveness of business decisions through the use of data and machine learning/predictive modeling
Executes on modeling/machine learning projects effectively
Reviews and evaluates on appropriateness of techniques, given current modeling practices, to senior leadership

Currently pursuing a PhD degree in a quantitative field (e.g. mathematics, statistics, computer or physical science)
Knowledge of reinforcement learning is required from coursework
Familiar with MDP framework such as value, action value function, reward, Monte Carlo methods
Experience with implementing model free reinforcement learning algorithms, especially policy gradient method, including REINFORCE, Actor-Critic methods, Proximal policy optimization
Demonstrated fluency with Python programming and ability to run simulation
Knowledge of programmatic advertising business is a strong plus
Experience with cloud computing services such as Vertax AI, Sagemaker or data warehouse service like BigQuery is a plus

Data Science Advertising Intern

Key skills