Data Scientist
Be Different
Johannesburg, ZA
5d ago

A well-established Client is looking for a Data Scientist to frame and solve business problems, and pursue business opportunities through a data-

centric approach. To design, develop and assist in implementation of analytics-based solutions utilizing machine learning & deep learning statistical and mathematical-

based analytical techniques and methodologies.

Requirements :

A minimum of three (3) years’ experience utilizing data manipulation software :

  • Base SAS
  • SAS Enterprise-Guide.
  • PLEASE NOTE : - MS Excel or MS Access does not constitute data-manipulation software.
  • A minimum of two (2) years’ experience utilizing data mining or statistical modelling software :

  • MatLab
  • SAS Enterprise-Miner
  • SPSS
  • Open-source data mining & machine learning tools, such as :
  • Responsibilities :
  • Random Forest
  • Random Forest
  • Stochastic Gradient Boost
  • XGBoost
  • Light Gradient Boost
  • Support Vector Machines (SVM)
  • Back Propagation Neural Nets
  • Feed Forward Neural Nets
  • Radial Basis Function Neural Nets
  • Recurrent Deep Neural Nets
  • Recursive Deep Neural Nets
  • Convolutional Deep Neural Nets
  • Linear Regression modelling
  • Logistic Regression modelling
  • Multi-Nomial Logistic Regression
  • Survival Analysis Cox Proportional Hazard, and Kaplan Meier Limit Estimator
  • Bayesian Statistics
  • Partial Least Squares
  • Correspondence Analysis
  • K-Nearest Neighbor modelling
  • Up-Lift Modelling
  • Dimensionality Reduction :
  • Factor Analysis,
  • Principal Component Analysis,
  • Linear Discriminant Analysis,
  • LARS - Least Angle Regression
  • LASSO - Least Absolute Shrinkage and Selection Operator
  • SMOTE - Synthetic Minority Over-sampling Technique
  • Model calibration :
  • Isotonic Regression
  • Platt Scaling
  • Gradient Descent optimisation
  • Stacked Ensemble Modelling
  • K-means Clustering
  • Gaussian Mixture Models
  • Density-Based Spatial Clustering of Applications with Noise (DBSCAN)
  • Association Pattern Mining
  • Sequence Pattern Mining
  • Text Mining natural language processing (NLP)
  • Entity Linkage (graphing)
  • Mixed-effects modelling
  • Two-Stage modelling
  • Time Series modelling :
  • Exponential Smoothing,
  • ARMA autoregressive moving average,
  • ARIMA autoregressive integrated moving average,
  • LSTM long-short-term-memory neural networks
  • Q-Learning algorithm
  • Markov Decision Process modelling
  • Differential Equation modelling
  • Integral Equation modelling
  • Algebraic Equation modelling
  • Linear Programming
  • Non-Linear Programming
  • Integer Programming
  • Network Flow modelling
  • Stochastic Process modelling
  • Markov
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