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Title

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Python Developer Data Scientist Social Media Sensing Behaviour Intervention

Description

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We are looking for a Python Developer Data Scientist specializing in Social Media Sensing and Behaviour Intervention to join our innovative team. In this role, you will leverage your expertise in Python programming, data science, and behavioral analytics to extract, analyze, and interpret large-scale social media data. Your primary objective will be to design, implement, and evaluate data-driven interventions that positively influence user behavior across various social media platforms. You will work closely with cross-functional teams, including behavioral scientists, UX researchers, and software engineers, to develop algorithms and models that detect behavioral patterns, sentiment, and emerging trends. Your work will directly inform the creation of targeted interventions, such as nudges, notifications, or content recommendations, aimed at improving user well-being, promoting healthy online habits, or mitigating harmful behaviors. Key responsibilities include building robust data pipelines for real-time social media data ingestion, developing machine learning models for behavior prediction and intervention efficacy, and visualizing insights for stakeholders. You will also be responsible for ensuring data privacy and ethical use of information, adhering to best practices and regulatory requirements. The ideal candidate has a strong background in Python, experience with social media APIs, proficiency in machine learning and natural language processing, and a passion for applying data science to real-world behavioral challenges. Familiarity with A/B testing, experimental design, and digital health interventions is highly desirable. Excellent communication skills and the ability to translate complex findings into actionable recommendations are essential. This is an exciting opportunity to make a tangible impact on digital communities by harnessing the power of data science and behavioral insights. If you are driven by curiosity, innovation, and a desire to improve online experiences, we encourage you to apply.

Responsibilities

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  • Develop and maintain Python-based data pipelines for social media data collection.
  • Analyze large-scale social media datasets to identify behavioral patterns.
  • Design and implement machine learning models for behavior prediction.
  • Collaborate with behavioral scientists to create effective interventions.
  • Evaluate the impact of interventions using statistical and experimental methods.
  • Visualize and communicate insights to technical and non-technical stakeholders.
  • Ensure ethical use and privacy of social media data.
  • Stay updated on latest trends in data science and behavioral analytics.
  • Document processes, models, and findings clearly.
  • Support deployment of interventions on digital platforms.

Requirements

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  • Bachelor’s or Master’s degree in Computer Science, Data Science, or related field.
  • Proficiency in Python and relevant data science libraries (e.g., pandas, scikit-learn, TensorFlow).
  • Experience with social media APIs and data extraction.
  • Strong knowledge of machine learning and natural language processing.
  • Familiarity with behavioral science concepts and intervention strategies.
  • Ability to design and analyze A/B tests and experiments.
  • Excellent problem-solving and analytical skills.
  • Strong communication and teamwork abilities.
  • Understanding of data privacy and ethical considerations.
  • Experience with data visualization tools (e.g., matplotlib, Tableau) is a plus.

Potential interview questions

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  • Describe your experience working with social media data.
  • What machine learning techniques have you used for behavior prediction?
  • How do you ensure data privacy and ethical use in your projects?
  • Can you provide an example of a successful behavioral intervention you designed or analyzed?
  • What Python libraries do you prefer for data science tasks and why?
  • How do you communicate complex data insights to non-technical audiences?
  • Describe your experience with A/B testing or experimental design.
  • What challenges have you faced in social media data analysis and how did you overcome them?