InvoiceCloud
Fintech
DataScientist
Neural analysis suggests this role is
optimal for Mid candidates.
“Data Scientist at InvoiceCloud. Skills: Machine learning, Data science, Model deployment. Design ML models. Develop ML models”
What You'll Achieve.
Improve digital payment adoption; Improve customer engagement; Improve billing operations; Drive measurable business outcomes
Industry & Context.
Business problems
What You'll Do.
Deploy models to production
Monitor deployed models
Track performance metrics
Set up scoring pipelines
Set up inference pipelines
Integrate models into production systems
Communicate model results
Communicate business impact
Contribute to A/B testing
Contribute to experimentation frameworks
Measure business impact
How You'll Work.
Team & Collaboration
Cross-functional teams
Communication Scope
Communicate results; Communicate impact
Full Job Description
About InvoiceCloud: InvoiceCloud is a fast-growing fintech leader recognized with 20 major awards in 2025, including USA TODAY and Boston Globe Top Workplaces, multiple SaaS Awards wins for Best Solution for Finance and FinTech, and national customer service honors from Stevie and the Business Intelligence Group. Judges also highlighted our mission to reduce digital exclusion and restore simplicity and dignity to how people pay for essential services, as well as our leadership in AI maturity and responsible innovation. It’s an award-winning, purpose-driven environment where top talent thrives. To learn more, visit InvoiceCloud.com. Job Description: Data Scientist (4+ Years Experience) As a Data Scientist at InvoiceCloud, you will work on data science projects that directly improve digital payment adoption, customer engagement, and billing operations for utilities, government, and insurance clients across the US. You will collaborate with cross-functional teams to design, develop, and implement machine learning models that drive measurable business outcomes. We are looking for candidates who have worked on large-scale data projects and have experience contributing to model development and deployment — with a willingness to take on increasing ownership of the model lifecycle over time. Key Responsibilities: Design, develop, and deploy ML models to address business problems. Utilize Python and SQL for data manipulation, analysis, and modeling using NumPy, Pandas, and Scikit-learn. Work with Snowflake or similar large-scale data platforms for extraction, transformation, and preparation. Own the full model lifecycle — from problem framing and feature engineering through to production deployment. Monitor deployed models post-production: track performance metrics, detect drift, and trigger retraining as needed. Set up scoring and inference pipelines for large-scale data, covering batch and real-time workflows. Collaborate with data engineers to integrate models into produc
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