Role overview
The Growth team is responsible for driving user & revenue growth for Robinhood. As we expand our suite of product offerings, we want to make sure we are taking a personalized approach to driving growth & engagement, by helping each user discover & engage with the right products & features within Robinhood that they might find most valuable. As we embark on this path, we are looking for a senior MLE to come in and lead our personalization efforts, and conceive, build & execute on a roadmap for how to effectively personalize our app experiences to drive user growth & engagement.
What you'll work on
As a Machine Learning Engineer in our team, your primary focus will be on the implementation and evaluation of machine learning algorithms through rigorous experimentation and testing methodologies. Your responsibilities will include:
- Model Development and Implementation: Develop and fine-tune machine learning models, with a focus on ranking and scoring. Ensure these models are scalable and efficient.
- Development of Reinforcement Learning Models: Design and implement reinforcement learning algorithms to optimize decision-making processes in dynamic environments.
- A/B Testing and Experimentation: Design and conduct A/B tests to assess the performance of different machine learning models. This includes setting up the test environment, monitoring performance, and analyzing results.
- Multi-Armed Bandit Implementation: Apply multi-armed bandit strategies for real-time decision-making in our algorithmic processes. Balance the trade-off between exploration of new strategies and exploitation of known successful approaches.
- Bayesian Optimization Techniques: Utilize Bayesian optimization for hyperparameter tuning and model optimization. Focus on achieving higher efficiency in model selection and parameter optimization.
- Data Analysis and Insight Generation: Analyze experimental data to extract actionable insights. Use statistical techniques to validate the findings and ensure their relevance and accuracy.
- Cross-Functional Collaboration: Work closely with other engineering teams, data scientists, and product managers to integrate machine learning models into the product and ensure they meet business requirements.
- Documentation and Reporting: Maintain comprehensive documentation of models, experiments, and findings. Prepare reports and presentations to communicate results to different stakeholders.