What are the responsibilities and job description for the Data SCientist - FTE - Hybrid position at Acunor?
Responsibilities
- Conduct research leveraging big data technologies that surface actionable insight that influence analytical solutions roadmap
- Gather and process raw data at scale by using statistical packages like R, and programming language like Python, Scala
- Process unstructured data into a form suitable for analysis and then do the analysis.
- Comfortable using advanced functions in MS Excel for quick analysis / manipulation / insight.
- Use predictive modeling for forecasting demand, work load and provide actionable insights.
- Implement Natural Language Processing tools and algorithms to solve automation problems.
- Implement Computer Vision and Deep learning techniques to solve automation problems.
- Critical thinking skills to assess how AI capabilities can best be applied to complex business situations.
- Work closely with other functional teams to integrate your ideas, innovations and algorithms into production systems.
- Support business decisions with ad hoc analysis as needed.
- Having the ability to query databases with structured and un-structured data and perform statistical analysis
- Utilize cutting edge algorithms in Natural Language Processing (NLP) and Computer Vision for implementing automation in production. Notions about Bayesian and Kernel methods is a strong plus
- Apply both Statistical Modeling and Optimization methods, separately or in combination.
- Two or more years of Industry experience / similar experience in academics in the field of Data Science
- Master's degree in Computer Science or related field or equivalent work experience, PhD being a significant plus
- Courses in AI / Machine learning / Deep learning from an University or MOOC (like Udacity, Coursera)
- A strong drive to learn and master new technologies and techniques in AI
- Ability to translate recent scientific papers to code (python, R)
- Some experience in software or applications engineering and/or technical operations
- Work and/or academic experience building applications using any of the following:
- Large scale distributed databases as well as more traditional options: key-value, graph, SQL, NoSQL, time series
- Data cleansing, manipulating datasets
- Training various Machine Learning, Deep Learning algorithms for NLP, Image processing, Time series and predictive analytics
- Exposure to cloud environments preferably Microsoft Azure
- Experience handling data with relational databases is preferred
