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Senior Data Scientist

Stellar IT SolutionsSt. Louis, MO🇺🇸United StatesPosted Oct 1, 2026

Quick Overview

Seniority
Mid Senior
Work mode
Hybrid
Location
St. Louis, MO, United States
Posted
21 hours ago
SQLMLflowMachine LearningNLPNumPyScikit-learnPandasPython

Job Description

Senior Data Scientist - Applied Machine Learning

Remote

3-6+ Months

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.

Required Skills

Machine Learning / Statistics

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

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.

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