Senior Data Scientist

Hands-on senior technical resource on a two-person KCS project team focused on developing an ML solution for identifying high-value Cisco Learning engagement opportunities.

Traditional predictive ML

• Statistical modeling
• Classification / probability-based modeling
• Feature engineering and feature selection
• Feature importance / ablation analysis
• Model evaluation and calibration
• Holdout and temporal validation
• Leakage identification and prevention
• Logistic Regression
• Scikit-learn or comparable ML framework

Traditional ML, feature engineering, statistical modeling, classification/probability analysis, class imbalance, and seasonality as key areas of need.

Advanced Python

• SQL
• Num Py
• Relational/database analysis
• Structured + unstructured data

ML training/evaluation workflows

• Experiment tracking
• Automated testing/retraining concepts
• Production-oriented ML practices

The operating model states that Kforce work will include understanding existing signals, building experiments against individual data sources, determining associated features/dimensions, and designing/proposing feature-engineering ML stages. Deep ML expertise is mandatory.

Sales-domain feature engineering / predictive analytics

• Propensity modeling / opportunity or lead scoring
• Revenue-oriented predictive analytics
• Customer adoption, consumption, or renewal modeling
• Survival / time-to-event analysis
• NLP / text analytics
• MLflow
• Feature Store concepts
• Enterprise data and governance

Important recruiter emphasis:
Sales-domain ML/feature-engineering experience should be treated as especially important. Client Stakeholder explicitly wrote that“Experience and Expertise in ML, Feature Engineering in Sales Domain is must.”

Skills :