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# Data Scientist - Applied Machine Learning

- **Company:** Stellar IT Solutions
- **Location:** Saint Louis, United States (Remote available)
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Automation of Tests, Cloud Computing, Database Analysis, Monitoring of Systems, Python (Programming Language), Logistic Regression, Machine Learning, NumPy, Standard Sql, Unstructured Data, Management of Software Versions, Enterprise Data Management, Feature Store, Feature Engineering, Boosted Trees, Model Validation, Pandas, Scikit Learn, Xgboost, Feature Selection, Machine Learning Operations, Feature Importance, Text Analysis, Cisco
- **Published:** October 2, 2026
- **Apply:** https://www.dice.com/job-detail/06ef92fb-a1f2-491d-9c50-cba4496bcd3a

## About the Role

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., Machine Learning / Statistics, 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.

## Description

Traditional predictive ML

 * Statistical modeling
 * Classification / probability-based modeling
 * Feature engineering and feature selection
 * Feature importance / ablation analysis
 * Model evaluation and calibration
 * Class-imbalance techniques
 * Holdout and temporal validation
 * Leakage identification and prevention
 * Logistic Regression
 * Gradient Boosted Trees, including XGBoost/LightGBM
 * Scikit-learn or comparable ML framework

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

Languages / Data

Advanced Python

 * SQL
 * Pandas
 * NumPy
 * Relational/database analysis
 * Structured + unstructured data

ML Lifecycle

ML training/evaluation workflows

 * Feature-engineering pipelines
 * Model monitoring
 * Model registries/versioning
 * 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.

Preferred Skills

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
 * Cloud ML ecosystem exposure
 * Enterprise data and governance
 * Prior Cisco experience