About the Role
The role involves designing and implementing machine learning solutions that directly address customer challenges, working across the full lifecycle from ideation to deployment in production environments.
Responsibilities
- Develop and deploy machine learning models tailored to customer use cases
- Collaborate with product managers to define problem scope and success metrics
- Translate business requirements into technical specifications
- Conduct exploratory data analysis to uncover patterns and insights
- Design experiments to validate model performance in real-world settings
- Optimize models for accuracy, scalability, and speed
- Work with engineering teams to integrate models into production systems
- Monitor model behavior post-deployment and implement improvements
- Ensure data quality and integrity across pipelines
- Communicate technical findings to non-technical stakeholders
- Lead efforts to improve data labeling and annotation processes
- Support the creation of customer-facing data products
- Evaluate new data sources for potential integration
- Maintain documentation for models and workflows
- Drive best practices in model versioning and reproducibility
- Contribute to ethical AI guidelines and bias mitigation strategies
- Participate in peer reviews of modeling approaches
- Stay current with advancements in applied machine learning
- Mentor junior team members on technical and methodological topics
- Support customer onboarding with technical expertise
- Assist in defining roadmap for data science capabilities
- Use statistical methods to measure impact of data products
- Troubleshoot issues in data pipelines affecting model inputs
- Work with UX teams to visualize model outputs effectively
- Ensure compliance with data privacy standards
Compensation
Competitive salary and benefits package
Work Arrangement
Remote with flexible hours
Team
Cross-functional team focused on data-driven product development
What We Value
- Practical problem-solving over theoretical perfection
- Clear communication across technical and non-technical roles
- Ownership of end-to-end project delivery
- Curiosity and continuous learning
- Collaborative approach to team challenges
Technology Stack
- Python for model development
- TensorFlow and PyTorch for deep learning
- Airflow for workflow orchestration
- BigQuery and Snowflake for data storage
- Kubernetes for model deployment
- MLflow for model tracking
- Looker for data visualization
- GitHub for version control
- Docker for containerization
- Google Cloud Platform as primary infrastructure
Onboarding Process
- Structured onboarding plan over first 90 days
- Pairing with a senior team member for guidance
- Deep dives into existing models and systems
- Introduction to key stakeholders across departments
- Access to internal documentation and training resources
Available for qualified candidates
