Staff Data Scientist
Windfall
Responsibilities:
- Architect and Own Modeling Solutions: You will lead the design and own the end-to-end lifecycle of our most sophisticated machine learning models, from ideation and strategic planning through to deployment and long-term performance monitoring.
- Synthesize Diverse Datasets: You will become an expert at blending first-party customer data with Windfall's proprietary data assets to engineer novel features and build highly predictive models.
- Build for Scale: You will go beyond individual models to design and build scalable, reusable data pipelines and modeling frameworks that improve the efficiency and capabilities of the entire team.
- Innovate and strategize: Address complex, ambiguous problems and craft creative modeling solutions to meet diverse customer needs across various industries.
- Communicate with Impact: You will be responsible for clearly communicating model performance, methodology, and business impact to both technical and non-technical stakeholders.
Requirements:
- 8+ years of professional industry experience in data science, with a proven track record of leading complex machine learning projects from conception to completion.
- Deep, expert-level knowledge of machine learning theory and its practical application, including extensive experience with classification, regression, and feature engineering.
- Mastery of Python and its data science ecosystem.
- Expert-level SQL skills, with the ability to write optimized queries against large-scale datasets.
- A strategic, product-driven mindset with a history of influencing product roadmaps and tying data science initiatives directly to business outcomes.
- Hands-on experience with the engineering aspects of data science, including proficiency with Docker for containerization and experience building and deploying data pipelines.
- Eligible to work within the United States.
Preferred Qualifications:
- Experience designing and implementing MLOps best practices for model monitoring, versioning, and automated retraining.
- Familiarity with modern cloud data platforms (e.g., BigQuery) and pipeline orchestration tools (e.g., Airflow).
- Experience working in marketing analytics, ad-tech, fintech, or for a nonprofit organization.
- A Master's degree or Ph.D. in a quantitative field such as Statistics, Computer Science, Economics, or Mathematics.