Create and maintain optimal data pipeline architecture,
Identify, design, and implement internal process improvements: automating manual processes, optimizing data delivery, re-designing infrastructure for greater scalability.
Experience/knowledge of employing statistical, data science and machine learning algorithms on real-world problems.
Work with stakeholders including the Executive, Product, Data and Design teams to assist with data-related technical issues and support their data infrastructure needs
Work with product team, data science team and data engineer team to analyze product features, track user behaviors to drive feature enhancement or new feature development.
Identify and develop data collection strategies to support reporting on key performance indicators that directly measure online campaigns, website and conversion funnel performance
Designing and implementing automated reporting solutions across multiple teams and stakeholders. Translate the reports into business values and provide suggestions to drive effective business decisions.
Work with data engineer team on daily data quality monitoring and data validation. Create documentation and reports on data quality and data validation)
Supporting cross-functional teams on the day-to-day reporting/ visualization/ data analysis execution of different implementations.
Master’s degree in Machine Learning, Data Science, Computer Science, Mathematics, Statistics, or related engineering field or a bachelor’s degree with a significant amount of relevant work experience.
Retrieving and analyzing data using SQL/Python from SQL or NoSQL database such as SSMS, Postgres, MongoDB, Cassandra, etc.
Hands-on knowledge of data modelling tools, data mapping tools, and data profiling tools.
Experience with and theoretical understanding of algorithms for supervised and unsupervised modeling such as classification, regression, clustering, recommendation engine and anomaly detection
Experience in Python programming and familiarity with python libraries such as numpy, pandas, scikit-learn etc.
Expertise with statistical data analysis. (e.g. linear models, multivariate analysis, stochastic models, sampling methods, A/B testing)
Experience in deploying machine learning products in production using docker is a plus.
Experience supporting and working with cross-functional teams in a dynamic environment.
Experience with object-oriented/object function scripting languages: Python preferable.
Strong in BI technologies: e.g. Microsoft Power BI (preferable), Tableau, Google Analytics.
Experience building and optimizing ‘big data’ data pipelines, architectures and data sets, Azure Cloud experience preferred.