Major responsibility:
- Collaborate with product design and engineering teams to understand and address data-related needs.
- Develop and maintain data processing systems and pipelines to ensure efficient data flow and accessibility.
- Design and implement innovative statistical models and data science algorithms for data analysis.
- Analyze high-volume, unstructured data sources to build structured models for product quality assessment.
- Create visualizations and dashboards to communicate data insights to stakeholders.
- Ensure data integrity and security throughout the data lifecycle.
- Stay current with industry trends and advancements in data science and engineering.
Good to have:
Programming Languages: Proficiency in languages such as Python, R, Java, and SQL is essential for data manipulation, analysis, and building data pipelines
Statistical Analysis: Ability to apply statistical methods to analyse and interpret complex data sets
Optimization: Understanding of different optimization techniques and familiarity with Gurobi
Data Visualization: Skills in using BI tools like Tableau, Power BI, or similar to create insightful visualizations and dashboards
Data Processing Frameworks: Familiarity with big data technologies like Apache Spark, Hadoop, and Kafka is crucial for handling large datasets
ETL Processes: Knowledge of Extract, Transform, Load (ETL) tools and processes to ensure efficient data flow and integration
Familiarity with machine learning libraries and tools such as TensorFlow, Scikit-learn, and PyTorch for developing predictive models
