Technical Skills

  1. Data Manipulation:
    • How do you handle missing data in a dataset? Can you walk us through the steps you would take in cleaning and preprocessing data?
    • Which Python libraries do you frequently use for data manipulation, and why?
  2. Exploratory Data Analysis (EDA):
    • Can you describe a recent project where you conducted exploratory data analysis (EDA)? What insights did you uncover, and how did they impact decision-making?
  3. Predictive Modeling:
    • Walk us through a machine learning model you developed. What problem were you solving, and how did you evaluate and optimize the model’s performance?
    • How do you decide which machine learning algorithm to use for a particular problem? Can you provide examples?
  4. Statistical Knowledge:
    • How do you apply statistical techniques to solve business problems? Can you provide an example where you used statistical models to generate actionable insights?
  5. A/B Testing & Experimentation:
    • Have you ever designed or analyzed an A/B test? What challenges did you face, and how did you interpret the results?
  6. Data Visualization:
    • What tools do you prefer for data visualization, and how do you ensure that your visualizations are accessible to non-technical stakeholders?
  7. Cloud Platforms:
    • Describe your experience using cloud platforms like AWS, GCP, or Azure for deploying machine learning models. What challenges did you encounter?
  8. Model Deployment:
    • Can you share a project where you deployed a machine learning model into production? How did you monitor and maintain it post-deployment?

Problem-Solving & Research

  1. R&D in Data Science:
    • Describe a situation where you had to perform research and development in data science to improve a model or methodology. How did your findings enhance your project?
  2. Complex Problem Solving:
    • Tell us about a time you faced a complex data problem. How did you approach it, and what was the outcome?

Collaboration & Communication

  1. Stakeholder Engagement:
    • How do you communicate technical findings to non-technical stakeholders? Can you give an example of how you presented data insights to influence a key decision?
  2. Cross-functional Collaboration:
    • Describe a project where you worked closely with different teams (e.g., program, field teams) to integrate data insights into program strategies.
  3. Mentoring:
    • Have you ever mentored junior data scientists? What strategies do you use to help them improve their data science skills?

Nonprofit & Social Impact Focus

  1. Nonprofit Sector Experience:
    • What experience do you have working in the nonprofit sector, and how does it influence your approach to data science?
  2. Theories of Change:
    • How do you apply Theories of Change (TOC) in data science work? Can you provide an example where TOC guided your data analysis?
  3. Participatory Research:
    • Have you been involved in participatory research with last-mile communities? How did you approach data collection and analysis in such contexts?

Behavioral & Soft Skills

  1. Adaptability:
    • Tell us about a time when you had to adapt to a significant change in project priorities. How did you manage the shift?
  2. Problem-Solving Under Pressure:
    • Describe a situation where you had to solve a data problem under tight deadlines. How did you manage your time and resources?
  3. Team Player:
    • How do you balance independent work with collaboration when working on data science projects? Can you give an example?

Continuous Learning

  1. Staying Updated:
    • How do you stay current with the latest developments in data science and machine learning? What’s the most recent tool or technique you’ve learned?

These questions will help assess the technical skills, problem-solving capabilities, collaboration style, and nonprofit experience required for the role.

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