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Data Science Engineer

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Roles & 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.

Required 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.