Data Science Engineer
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PRIMARY RESPONSIBILITIES
- 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.
QUALIFICATIONS
- 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.
23 days ago