Data Scientist SME

Job description

Job Description

Data Scientist SME

Position Description
CGI is seeking business analytics expertise to gather, conceptualize and prioritize the needs of its federal government customers in a form that is most meaningful to begin successful architecture and design in an easily understandable form in a legacy construct. Candidate shall utilize business intelligence (BI) tools, methodology and expertise that assists in detecting trends, patterns and non-obvious relationships in data contained in a multitude of systems and an enterprise data warehouse.

This position is responsible for business consulting activities for the Data Strategy and Analytics teams within our client organizations to monitor and assist in improving their analytics eco system. We need someone with creative problem-solving skills to work on our client’s business opportunities. Our Data Scientist will work to understand high value opportunities and identify/build potential solutions, which often involve discovering new insights by transforming data into intuitive and interactive visualizations or applications

Your future duties and responsibilities
- Will play the role of Advanced Analytics Subject Matter Expert as part of an overall project team rolling out a standard service for Advanced Analytics.
- Develop/Support Advanced Analytics team members with algorithm development.
- Lead the engagement with US-based Advanced Analytics projects.
- Lead activities around emerging "big data" architectures for Advanced Analytics (e.g. Spark, Hadoop, etc.).
- Limited travel is to be expected.

Required qualifications to be successful in this role
- Due to the nature of the government contract requirements and/or clearance requirements, US citizenship is required
- 5-8 years of experience in related fields.
- Bachelors or Master’s degree in Mathematics (Statistics), Physics, Operational Research, Information Technology, Business Information Systems, Computer Engineering, Computer Information Systems or relevant field.

Experience with the following methods and tools to develop intelligence assets and processes that will drive operational outcomes:

- Statistical analysis: modeling and statistical tools that can help analysts discover patterns or generalizations in the data. This analysis can produce models that can be used to identify similar patterns in other data or common characteristics among seemingly disparate data.
- Geospatial analysis: visualization tools that can display a set of events or activities on a map showing streets, buildings, geopolitical borders or terrain. This analysis can help produce intelligence about the location or type of location that is favorable for a particular activity.
- Link analysis: visualization tools that can help analysts discover patterns of associations among various entities. This can produce a social network representation of the data.
- Temporal analysis: visualization tools that can display events or activities in a timeline to help an analyst identify patterns or associations in the data. This can produce a time sequence.
- Machine learning: data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.
- Predictive analysis: analysis that uses data to make predictions about unknown future events. Predictive analytics uses many techniques from data mining, statistics, modeling, machine learning and artificial intelligence to analyze current data to make predictions about future.
- Artificial intelligence: the simulation of human intelligence processes by computer systems. These processes include learning (the acquisition of information and rules for using the information), reasoning (using rules to reach approximate or definite conclusions) and self-correction.
- Analytics as a service (AaaS): the provisioning of analytics software and operations through web-delivered technologies.

Additional Experience:
- Statistical model or algorithm development.
- Large, complex datasets and using data for analytics for business improvement.
- Data analysis software (specifically statistical packages and numerical modelling tools, e.g. SAS, MatLab or R).
- Data mining and data extraction.
- Data model development for advanced analytics/statistics.
- Analytics for engineering (EBS, data driven models).
- Engineering surveillance, modelling, optimization.
- Artificial intelligence, big data, architecture.
- Hadoop, NoSQL, Spark, SAP HANA.
- Strong stakeholder management, interpersonal and influencing skills with ability to work across multiple business areas.
- Project management, process change and project delivery experience.
- Experience of working in a virtual team and with virtual stakeholders.
- Ability to combine high-level strategic, commercial and statistical thinking with a simple pragmatic approach for analytics.
- Independent analytical thinker - ability to challenge established thinking to stimulate creativity.

Desired:
- Doctorate in Computer science, Engineering, Physics or Statistics.

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Skills

Reference

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