Senior Data Scientist - Applied Machine Learning

Kforce as a client in San Jose, CA that is seeking a hands-on Senior Machine Learning Engineer/Data Scientist to join a small technical project team developing an ML solution focused on identifying and prioritizing high-value learning engagement opportunities. This role is ideal for a builder with deep experience in traditional machine learning, statistical modeling, feature engineering, and sales-oriented predictive analytics. Key Responsibilities:

• Analyze complex, high-dimensional datasets to identify meaningful predictive signals
• Develop and evaluate classification, probability-based, and statistical models
• Perform feature engineering, feature selection, importance analysis, and experimentation across multiple data sources
• Build repeatable ML training, evaluation, and feature-engineering workflows
• Connect model performance to actionable sales, revenue, customer adoption, and opportunity outcomes
• Collaborate with technical and business stakeholders in an iterative development environment
• Machine Learning: Deep hands-on experience with traditional predictive ML and statistical modeling, including classification, probability modeling, model evaluation/calibration, class imbalance, seasonality, temporal validation, and data leakage prevention
• Feature Engineering: Strong expertise in feature engineering, feature selection, feature importance, and determining which signals within complex, high-dimensional datasets provide meaningful predictive value
• Sales-Domain ML: Demonstrated experience applying machine learning and feature engineering within a sales, revenue, opportunity, or customer-focused domain; Experience connecting model outputs to actionable business outcomes is essential
• ML Techniques & Frameworks: Experience with Logistic Regression, Gradient Boosted Trees such as XGBoost/LightGBM, and Scikit-learn or comparable ML frameworks
• Python & Data: Advanced Python and SQL skills, including Pandas, NumPy, relational/database analysis, and experience working with structured and unstructured data
• ML Lifecycle: Experience developing ML training/evaluation workflows, feature-engineering pipelines, experiment tracking, model versioning/monitoring, and production-oriented ML practices
• Hands-On Development: Strong coding and analytical skills with the ability to independently explore data, build and test models, evaluate results, and iterate as data and business requirements evolve
• Important: This role requires deep applied machine learning expertise; GenAI/LLM experience can be complementary, but the primary focus is traditional ML, statistical modeling, feature engineering, and sales-oriented predictive analytics
• Experience with model evaluation/calibration, class imbalance, temporal validation, seasonality, and leakage prevention
• Experience with ML training/evaluation pipelines, experiment tracking, model monitoring, and versioning
• Ability to work with structured and unstructured enterprise data