Data Scientist

Position: Data Scientist Location: Montgomery Park – Portland, OR 1 Day On-site, 4 Days Remote Contract: 6 Month Contract-to-Hire Location: Portland (1 day a week) JOB DESCRIPTION • Develop, deploy, and maintain scalable machine learning models and analytical workflows using Databricks, Apache Spark, and cloud-based data platforms. • Partner with financial domain stakeholders to understand business challenges related to risk, compliance, fraud, lending, investments, and customer analytics. • Perform hands-on data exploration, feature engineering, statistical analysis, and modeling using Python, SQL, and Spark. • Collaborate with data engineering teams to build reliable data pipelines and ensure high-quality, production-ready datasets. • Communicate complex technical findings to non-technical audiences; present actionable insights to business leaders. • Apply deep financial services expertise to ensure models meet regulatory expectations (e.g., model documentation, audit, transparency, fairness). • Optimize workflows for performance and cost efficiency within Databricks and cloud environments. • Design experiments and evaluate model performance using best practices in ML Ops, version control, and reproducibility. REQUIRED SKILLS AND EXPERIENCE • Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Math, Engineering, or related field. • 3+ years of experience as a Data Scientist, preferably within banks, credit unions, fintech, or other financial institutions. • Hands-on experience with Databricks, Apache Spark, and cloud ecosystems (Azure, AWS, or GCP). • Strong proficiency in Python, SQL, and common ML frameworks (scikit‑learn, PyTorch, TensorFlow, etc.). • Deep understanding of financial data structures, regulatory requirements, and industry workflows. • Experience building, deploying, and monitoring machine learning models in production environments. • Ability to translate business problems into data-driven solutions with measurable impact. NICE TO HAVE SKILLS AND EXPERIENCE • Experience with ML Ops tools (MLflow, Azure DevOps, Databricks Repos, CI/CD pipelines). • Familiarity with risk scoring, fraud detection, AML/KYC analytics, credit modeling, or customer segmentation. • Knowledge of distributed systems, big data engineering, and scalable architecture design. • Strong communication and stakeholder management skills in financial environments.