invoicecloud
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: Senior Data Scientist (8+ Years Experience) As a Senior Data Scientist, you will lead and execute complex data science projects that drive meaningful business outcomes, working closely with cross-functional teams to design, develop, and implement advanced machine learning models. We are looking for candidates with a proven record of delivering end-to-end ML models on large-scale data — owning the full lifecycle from problem framing and development through to production deployment, post-launch monitoring, and continuous improvement. Key Responsibilities: Lead the design, development, and deployment of advanced ML models (classification, regression, clustering, time series, deep learning) across business verticals. Own the full model lifecycle end-to-end — problem framing, feature engineering, model development, production deployment, monitoring, and retraining. Productionise multiple models ensuring reliability, scalability, and maintainability; set the standard for how models go live in the organisation. Design and oversee post-deployment monitoring frameworks: performance tracking, drift detection, alerting pipelines, and automated retraining strategies. Architect and implement scoring and inference pipelines for large-scale data, covering both batch and real-time workflows. Utilize Python and SQL with libraries such as NumPy, Pandas, and Scikit-learn; apply deep learning techniques using PyTorch or TensorFlow. Work with Snowflake or similar large-scale data platforms for complex data extraction and transformation at scale. Define problem scope and translate ambiguous business questions into well-structured data science projects with clear success criteria. Mentor junior data scientists, conduct code reviews, and foster a culture of engineering rigour and continuous learning. Communicate complex model results, methodology, and business impact clearly to senior stakeholders and leadership.
Bachelor's or Master's degree in Computer Science, Statistics, Mathematics, or a related field. 8+ years of professional experience in data science with a strong focus on machine learning, advanced analytics, and ML engineering / MLOps. Proven record of working with large-scale data to deliver production-grade ML models with full end-to-end ownership. Mandatory: hands-on experience productionising multiple ML models with complete deployment ownership. Mandatory: demonstrated post-production monitoring experience — drift detection, performance tracking, and automated retraining pipelines. Technical Skills: Solid understanding of the end-to-end data science lifecycle — from data acquisition, EDA, and feature engineering through to model development, validation, deployment, and post-production monitoring. Strong working knowledge of the MLOps lifecycle — including experiment tracking, model versioning, CI/CD for ML, pipeline orchestration, and model governance. Proficiency in Python and SQL. Deep experience with Snowflake or similar large-scale data tools (e.g. BigQuery, Redshift, Databricks). Strong model development expertise with Scikit-learn, XGBoost, PyTorch, or TensorFlow. Proven experience productionising ML models — containerisation (Docker/Kubernetes), model serving, API integration. Experience architecting scoring and inference pipelines for large-scale batch and real-time data. Hands-on experience with post-production monitoring tools: MLflow, Evidently AI, Fiddler, or equivalent. Experience with PySpark or equivalent tools for large-scale data processing. Strong working knowledge of Git and cloud platforms (AWS, GCP, or Azure).
Experience building propensity models (e.g. churn, upsell, likelihood to purchase) or other marketing-driven model use cases. Prior experience or exposure to the payments domain — transaction data, payment behaviour analytics, or related modelling. Familiarity with Apache Airflow for orchestrating and scheduling ML pipelines. Exposure to tools such as dbt for data transformation, or FastAPI / Flask for model serving and building lightweight ML inference APIs. InvoiceCloud is committed to providing equal employment opportunities to all employees and applicants. We do not tolerate discrimination or harassment of any kind based on race, color, religion, age, sex, nationality, disability, genetic information, veteran or military status, sexual orientation, gender identity or expression, or any other characteristic protected under applicable laws. This commitment applies to all aspects of employment, including recruitment, hiring, placement, promotion, termination, layoff, recall, transfer, leave, compensation, and training. If you require a disability-related or religious accommodation during the application or recruitment process, and wish to discuss possible adjustments, please contact jobs@invoicecloud.com . Click here to review InvoiceCloud’s Job Applicant Privacy Policy. For recruitment agencies: InvoiceCloud does not accept unsolicited resumes from agencies. Please do not forward resumes to our job aliases, employees, or any other company location. InvoiceCloud is not responsible for any fees associated with unsolicited submissions.