Businesses need to modernize how they acquire, engage with and enable consumers. Prove's phone-centric identity tokenization and passive cryptographic authentication solutions reduce friction, enhance security and privacy across all digital channels, and accelerate revenues while reducing operating expenses and fraud losses.
Requirements
- Strong proficiency in Python (pandas, scikit-learn, PyTorch/TensorFlow).
- Strong background in statistical methods (supervised/unsupervised learning, classification models, etc.), experimental design, and data visualization (Looker) and storytelling.
- Strong proficiency in SQL (Snowflake) with an ability to work with raw data and collaborate with data engineering teams to optimize data pipelines.
- Proficiency with cloud platforms (AWS).
- Solid understanding of APIs, model deployment processes, and monitoring practices.
- Familiarity with other programming languages such as R, Java, and Go.
- Prior exposure to production ML environments and workflows.
Responsibilities
- Develop, validate, and tune statistical and machine learning models that solve complex business problems.
- Partner with engineers to ensure models are designed with production deployment in mind.
- Design experiments and evaluate models using robust statistical methodologies.
- Build and maintain dashboards that proactively monitor product efficacy and distribute key insights across product and customer domains.
- Collaborate with ML engineers on deployment pipelines, APIs, and infrastructure.
- Proactively monitor deployed models for drift, accuracy, and reliability.
- Provide insights and business recommendations based on model performance in production.
Other
- 5+ years of experience applying machine learning and statistics to business problems.
- Excellent communication skills with ability to bridge technical and business contexts.
- Strong problem-solving and proactive ownership mindset.
- Comfort working in cross-functional teams with engineers, product managers, and business leaders.
- Ability to balance quick iterations with building long-term scalable solutions.
