Why This Role Stands Out
This remote Senior Data Scientist role offers a fantastic opportunity to leverage your advanced Python and machine learning expertise to develop impactful solutions within the telecommunications industry. You'll thrive here if you excel at traditional predictive modeling, feature engineering, and statistical analysis, contributing directly to identifying high-value engagement opportunities. With a competitive hourly rate and the potential for extension, this position is an excellent step for your career growth.
Quick Overview
Job Description
POSITION: Senior Data Scientist
INDUSTRY: Telecommunications
LOCATION: Remote
DURATION: 3 Month ( Possibility for extension )
RATE: $70/HR C2C
Video
VISA:
REQUIRED SKILLS
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
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