Data Scientist

Responsibilities

1. Data Exploration and Cleaning:
   
   • Collect clean and preprocess raw data from various sources.
     
   • Identify missing values outliers and inconsistencies.
     
2. Statistical Analysis:
   
   • Apply statistical methods to analyze data trends correlations and distributions.
     
   • Conduct hypothesis testing and interpret results.
     
3. Machine Learning Modeling:
   
   • Develop predictive models using algorithms such as linear regression decision trees random forests and neural networks.
     
   • Optimize model performance through feature engineering and hyperparameter tuning.
     
4. Data Visualization:
   
   • Create clear and insightful visualizations (charts graphs dashboards) to communicate findings to stakeholders.
     
   • Use tools like Matplotlib Seaborn or Tableau.
     
5. Feature Engineering:
   
   • Engineer relevant features from raw data to improve model accuracy.
     
   • Handle categorical variables time series data and text data.
     
6. Collaboration:
   
   • Work closely with crossfunctional teams (product engineering business) to define project goals and requirements.
     
   • Communicate complex technical concepts to nontechnical stakeholders.
     
7. Model Deployment and Monitoring:
   
   • Deploy models into production environments (onpremises or cloud).
     
   • Monitor model performance and address issues promptly.
     
8. Continuous Learning:
   
   • Stay updated with the latest advancements in data science machine learning and AI.
     
   • Participate in conferences workshops and online courses.
     

Qualifications

• Education: Bachelor s or master s degree in Computer Science Statistics Mathematics or a related field.
  
• Experience:
  
  • 2 years of experience as a Data Scientist or similar role.
    
  • Proficiency in Python or R for data manipulation and modeling.
    
  • Familiarity with SQL for querying databases.
    
• Skills:
  
  • Strong analytical and problemsolving skills.
    
  • Knowledge of machine learning libraries (e.g. ScikitLearn TensorFlow PyTorch).
    
  • Excellent communication skills.