Current Openings

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.