Data Scientist - Python, ML & Predictive Modeling

Data Scientist – Python, ML & Predictive Modeling

Duration: 12 months

Work Model: 100% Remote

Programming & Tools

• Strong proficiency in Python (NumPy, Pandas, Scikit-learn, TensorFlow/PyTorch).
  
• Working knowledge of statistical analysis and modeling.
  
• Experience with Jupyter Notebooks, RStudio, and data visualization tools.
  
• Familiarity with SQL and data querying.
  

Machine Learning & AI

• Solid understanding of supervised and unsupervised machine learning algorithms.
  

• Hands-on experience with:
  

  • Regression (Linear, Logistic)
    
  • Classification (Decision Trees, Random Forests, SVM)
    
  • Clustering (K-Means, Hierarchical)
    
• Experience with deep learning frameworks such as TensorFlow and PyTorch is a plus.
  

Predictive Modeling

• Proven experience in predictive modeling and forecasting.
  
• Ability to build, validate, and deploy predictive models.
  

• Strong understanding of:
  

  • Feature engineering
    
  • Model evaluation techniques (ROC, Precision/Recall, Cross-Validation)
    
• Experience working with real-world datasets to derive actionable insights.
  

Statistics & Data Analysis

• Strong foundation in statistics and probability.
  
• Experience with hypothesis testing, regression analysis, and statistical modeling.
  
• Proficiency in data cleaning, transformation, and exploratory data analysis (EDA).
  

Data & Deployment (Preferred)

• Experience with cloud platforms such as AWS, Azure, or GCP.
  
• Familiarity with Docker and containerization is a plus.
  
• Exposure to MLOps practices and CI/CD for machine learning models.
  

Soft Skills

• Strong analytical and problem-solving skills.
  
• Ability to translate business problems into data-driven solutions.
  
• Effective communication and storytelling with data.
  
• Collaborative mindset with cross-functional teams.
  

Nice-to-Have

• Experience with big data technologies such as Spark and Hadoop.
  
• Exposure to NLP, computer vision, or time-series forecasting.
  
• Knowledge of model deployment APIs such as Flask and FastAPI.