Study new data sources and find insights/correlations to investigate how data can be used to solve new business challenges.
Create prototypes with data sets and provide guidance on leveraging and combining new data sources for new business insights.
Apply statistical analysis and modelling techniques on small and large datasets to solve specific business problems in diverse industrial domains.
Provide strategic leadership in selection of platforms, tools, techniques and processes in the practice of the data science discipline.
Collaborate with product owners / product managers from other business units and/or customers to translate business requirements into algorithmic requirements that can be implemented with statistical and machine learning techniques.
Provide mentorship to other data scientists in the team.
Own and drive contemporary best practices in applying and deploying data science at scale.
Participate in designing the data architecture.
Process, cleanse and verify the integrity of data used for analysis.
Execute analytical experiments methodically.
What You Should Bring
Advanced degree (Ph.D. preferred) in engineering, science, mathematics or related subjects
Expert knowledge of statistical programming languages such as R or Python, and SQL.
Readiness to work with engineering teams to develop prototypes of software products leveraging exploratory data analytics.
Expert knowledge of data visualization, using tools such as Tableau or PowerBI.
Expert knowledge of Experimental Design and Statistical Decision Theory.
Solid knowledge of distributed data storage and computing, including Hadoop or Cassandra.
Practical knowledge of cloud computing, including AWS (preferred) or Azure and highly scalable databases, such as Dynamo DB.
Knowledge of NoSQL databases, such as MongoDB.
Great communication skills in English.
5+ years working and communicating with business stakeholders as a trusted adviser in data science.
5+ years translating business requirements into data science problem statements and execution tasks.
In-depth understanding of the machine learning lifecycle.
Practical skills with agile software development methods such as Scrum and Kanban
Experience in executing advanced analytics for customers, including marketing mix analysis, segmentation, retention modelling, targeted marketing, basket analysis, next product recommendation, and so forth.
Experience with color science.
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