Product Engineering Data Sciences Architect

Collaborate with cross-functional teams to design, develop, and implement data-driven solutions that enhance the product offerings. 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