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 production systems reliably.
• Communicate model results and business impact clearly to both technical and non-technical stakeholders.
• Contribute to A/B testing and experimentation frameworks to measure business impact of model decisions.

Qualifications:

• Bachelor's degree in computer science, Statistics, Mathematics, or a related field.
• 3 to 5 years of professional experience in data science or applied analytics.
• Prior experience working with large-scale datasets in a professional setting.
• Must have deployed multiple ML models to production with post-deployment monitoring experience (drift detection, alerting, performance tracking).

Technical Skills:

• Proficiency in Python and SQL.
• Experience with Snowflake or similar large-scale data tools (e.g. BigQuery, Redshift, Databricks).
• Hands-on model development across supervised, unsupervised, and semi-supervised techniques using frameworks such as Scikit-learn, XGBoost, or similar.
• Experience with deep learning and NLP techniques (e.g. PyTorch, TensorFlow, Hugging Face).
• Experience productionising models — packaging, versioning, and deploying to serving infrastructure.
• Familiarity with post-production monitoring tools (model drift, data quality checks, performance dashboards).
• Proficiency with experiment tracking tools such as MLflow or Weights & Biases.
• Strong working knowledge of Git for version control and collaborative development.
• Familiarity with cloud platforms (AWS, GCP, or Azure).
• Curiosity and familiarity with recent research and emerging methods — we value candidates who stay current with the field.