Data Scientist Python, SQL, Azure

Job Description
Your role and responsibilities:

• Participate in data science projects from problem definition through solution delivery.
  
• Analyze business requirements and translate them into data-driven solutions and actionable insights.
  
• Collect, clean, validate, and prepare structured and unstructured datasets for analysis.
  
• Perform exploratory data analysis (EDA) to identify trends, patterns, and opportunities.
  
• Develop, evaluate, and optimize statistical and machine learning models for prediction, classification, clustering, and other analytical tasks.
  
• Design and conduct experiments to evaluate hypotheses and measure model performance.
  
• Communicate findings and recommendations to stakeholders through reports, visualizations, and presentations.
  
• Collaborate with Product Owners, business stakeholders, and engineering teams to define project goals and data requirements.
  
• Monitor model performance, analyze results, and recommend improvements based on new data and business needs.
  
• Stay up to date with advances in data science, machine learning, and analytics methodologies.
  

Preferred qualifications:

• Passion for applying data science to solve real-world problems, particularly in the education domain.
  
• Experience developing predictive models or conducting research in education, learning analytics, or related fields.
  
• Experience analyzing and modeling learning behavior, educational, or user interaction data.
  
• Strong knowledge of statistics, probability, experimental design, and machine learning algorithms.
  
• Experience with the end-to-end data science lifecycle, including data collection, preprocessing, feature engineering, model development, evaluation, and deployment.
  
• Proficiency in Python and common data science libraries (e.g., Pandas, NumPy, Scikit-learn, TensorFlow, or PyTorch).
  
• Experience working with SQL and relational or NoSQL databases.
  
• Experience with data visualization tools and libraries (e.g., Matplotlib, Plotly, Tableau, or Power BI) is a plus.
  
• Familiarity with MLOps, model deployment, or cloud platforms is an advantage but not required.
  
• MS or PhD in Computer Science, Data Science, Statistics, Mathematics, or a related quantitative field.

• At least 3+ years of experience in data science or a related field, with strong programming skills in Python, R, or similar languages.
  
• Experience applying statistical analysis, machine learning, and predictive modeling to solve real-world business problems.
  
• Strong understanding of statistics, probability, hypothesis testing, experimental design (A/B testing), and data analysis techniques.
  
• Experience with the end-to-end data science lifecycle, including data collection, preprocessing, feature engineering, model development, evaluation, and interpretation.
  
• Experience working with SQL and relational or NoSQL databases.
  
• Experience working with cloud computing platforms (e.g., AWS, Azure, or Google Cloud) is preferred.
  
• Experience using large language models (LLMs) and generative AI for analytical or business applications is a plus.
  
• Familiarity with data visualization tools and libraries (e.g., Matplotlib, Plotly, Tableau, or Power BI) is an advantage.
  
• Excellent communication skills in English; proficiency in Japanese is a plus.
  
• Strong background in Statistics, Mathematics, Computer Science, Data Science, or a related quantitative discipline.
  
• Passion for continuous learning and staying current with advances in data science, machine learning, and AI.
  
• Strong analytical thinking and problem-solving skills, with the ability to translate business requirements into data-driven insights and predictive models.
  
• Ability to communicate complex analytical findings effectively to both technical and non-technical stakeholders.
  
• A collaborative team player who is willing to support colleagues and work effectively in cross-functional teams.
  
• Flexible and adaptable, with the ability to contribute to different phases of a data science project as needed.