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Senior Data Scientist

Neural Strategic Solutions, Inc.United States🇺🇸United StatesPosted Oct 1, 2026

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

Salary
$70/hr
Seniority
Mid Senior
Work mode
Remote
Location
United States
Posted
19 hours ago
SQLMLflowMachine LearningNLPNumPyScikit-learnPandasPython

Job Description

POSITION: Senior Data Scientist

INDUSTRY: Telecommunications

LOCATION: Remote

DURATION: 3 Month ( Possibility for extension )

RATE: $70/HR C2C

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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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