Staff Data Scientist

At Windfall, data science is central to our mission as a people data and AI company. We aim to revolutionize how organizations perceive and utilize people data by providing leading commercial and nonprofit partners with accurate and actionable predictive models.
We are looking for a deeply technical Staff Data Scientist to join our Modeling & AI team. You will be a handsonkeyboard architect, designing and building sophisticated, scalable machine learning systems that power our model offerings. Youll spend your time tackling ambiguous problems, pioneering novel modeling techniques, and writing productiongrade code that elevates the technical bar for the entire team.
Your work will be directly applied, as you collaborate closely with customers to develop a range of modelsfrom sophisticated marketing and lead scoring models to donation likelihood models that empower top nonprofit organizations worldwide. This position is ideal for an individual who is passionate about deep technical exploration, solving challenging technical problems, and constructing robust, endtoend ML solutions that deliver immense value to our clients.
Architect and Own Modeling Solutions: You will lead the design and own the endtoend lifecycle of our most sophisticated machine learning models, from ideation and strategic planning through to deployment and longterm performance monitoring.
Synthesize Diverse Datasets: You will become an expert at blending firstparty customer data with Windfalls 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.
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 nontechnical stakeholders.
7+ years of professional industry experience in data science, with a proven track record of leading complex machine learning projects from conception to completion.
~ Deep, expertlevel 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.
~ Expertlevel SQL skills, with the ability to write optimised queries against largescale datasets.
~ A strategic, productdriven mindset with a history of influencing product roadmaps and tying data science initiatives directly to business outcomes.
~ Handson experience with the engineering aspects of data science, including proficiency with Docker for containerisation and experience building and deploying data pipelines.
~ 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., D. in a quantitative field such as Statistics, Computer Science, Economics, or Mathematics.
$The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position across California. Within the range, individual pay is determined by work location and additional factors, including jobrelated skills, experience, and relevant education or training. We also offer a comprehensive benefits package, which you can explore on our careers site.
California: Salary range is $200,000 - $230,000 + Equity
Windfall is a people intelligence and AI company that gives gotomarket teams actionable insights. By democratising access to people data, organisations can intelligently prioritise gotomarket resources to drive greater business outcomes. Powered by bestinclass machine learning and artificial intelligence, Windfall activates insights into workflows that engage the right people for each respective organisation.