Data Scientist - Marketing

Responsibilities

• Extract and analyze data from databases and data warehouses to generate actionable insights for departmental stakeholders.
• Develop and maintain data pipelines and analytical datasets (using SQL and tools like dbt) to ensure data is prepared and structured for analysis.
• Apply data cleaning, modelling, and feature engineering techniques to prepare data for AI models.
• Build and validate basic machine learning models (for example, using Python's scikit-learn) to address business problems such as predictions or classifications.
• Perform advanced data manipulation and feature engineering using Python (pandas) and SQL to handle large datasets and complex transformations.
• Apply statistical techniques and quantitative analyses (e.g., A/B testing, regression analysis, time-series forecasting) to inform decision-making.
• Deliver end-to-end analytics projects, from understanding requirements to delivering final reports or model outputs, ensuring clarity and accuracy at each step.
• Collaborate with peers and stakeholders to interpret results, answer follow-up questions, and ensure that insights are understood and utilized in decision-making.
• Clearly communicate findings and technical concepts to non-technical stakeholders, tailoring the level of detail to the audience.
• Apply software engineering practices in high quality, clean, maintainable code that implements reproducible research and solution.

Qualifications

• Bachelor’s or Master’s degree in a relevant field (e.g., Computer Science, Mathematics, Physics).
• At least 3 years of experience in a data science or advanced analytics role.
• Strong skills in data analysis with Python (including libraries like pandas, numpy, scikit-learn) and proficient SQL ability.
• Solid understanding of statistics and experience with experimental design and analysis (for instance, A/B testing).
• Hands-on experience developing and evaluating machine learning models on real-world datasets.
• Ability to transform and clean large datasets, and knowledge of data pipeline or ETL processes.
• Excellent communication skills to explain complex analytical results to business stakeholders, and a collaborative approach to problem-solving.