Data Scientist

W2 ONLY position, we cannot accept C2C or 1099 candidates for this role Data Scientist Role Summary We are looking for a Mid-Level Contract Data Scientist to join our team and support ongoing analytics initiatives. This is a tactical, execution-focused role. We need a hands-on contributor who is comfortable working with our specific tool stack and domain to deliver practical modeling solutions. You will be responsible for the "grunt work" of building, validating, and deploying models rather than high-level research or strategy. The Ideal Candidate • You have enough experience to work independently but are happy executing on defined tickets and objectives. • You understand that the goal is a working model in production, not a perfect academic experiment. • Target Experience Level: 2–5 Years. Top 3 Requirements (Must-Haves) 1. Domain Familiarity: Experience in Mortgage, Lending, or Fintech is highly preferred. We need someone who understands the basic concepts of our data (loans, borrowers, credit risk) so they can start immediately. 1. Tool Stack: You must be proficient in Azure Databricks, Python, and SQL. You should be comfortable writing queries and running notebooks on Day 1. 1. Applied Focus: Experience writing code that actually runs in a business environment (not just local notebooks). Key Responsibilities • Model Building: Develop standard predictive models (Regression, XGBoost, etc.) to support business operations. • Data Preparation: Handle data cleaning, feature engineering, and data wrangling within Azure Databricks. • Deployment Support: Write clean, modular code that allows engineers to move your models into production. • Validation: Run performance monitoring and validation tests on existing and new models. Technical Qualifications • Experience: 2–5 years of professional Data Science experience. • Core Skills: Strong Python and SQL skills are non-negotiable. • Tech Stack: Experience with Azure Databricks and standard ML libraries (Scikit-Learn, XGBoost). • Education: Bachelor’s degree in a quantitative field or equivalent practical experience. Deliverables • Working model pipelines and notebooks. • Clean, commented code commits. • Documentation of model results and feature logic