Data Scientist - Assistant Manager (Hybrid)

Qualifications:
• Data Scientist (expert on data modeling and predictive analytics) and not Data Analyst or Data Visualization Specialist
• Knowledgeable on use of Python for use in Machine Learning and Data Modeling – Predictive Modeling, a Must background
• With a background in automation
Responsibilities:

Quantitative Modeling
a. Apply data analysis, data mining and data processing to present data clearly
b. Apply data mining techniques and develop software to investigate leads, identify patterns and regularities in data
c. Develop data models based on advanced statistical modelling, data mining, and machine learning methods
Business Problem Framing: Manage the problem definition and hypothesis formulation process

Analytics Problem Framing
a. Assist in the conceptualization of analytical projects
b. Break down problems to recompose them in ways that are solvable
c. Partner with stakeholders to translate business problems into data science projects
d. Plan the development of analytical solutions from initial design through implementation, prototyping and testing
e. Provide advice on the development of data analysis models based on project requirements
f. Provide subject matter expertise to stakeholders, and maintain an advanced knowledge of trends affecting the industry

Insights Generation
a. Assist with the development of actionable recommendations
b. Evaluate experiment outcomes to draw actionable conclusions
c. Guide stakeholders on how to act on findings
d. Synthesize findings into actionable insights
e. Develop compelling, logically structured presentations including storytelling of research and/or analytics findings

Project Management
a. Assist in planning project timelines and resources needed, and setting work quality guidelines
b. Deliver projects in line with agreed standards, providing fit-for-purpose solutions within time, quality, and budget constraints
c. Prepare documentation to outline data sources, models and algorithms used and developed

Information Systems Implementation
a. Develop prototypes and proof of concepts for the selected solutions
b. Assist with developing new data-discovery tools
c. Implement automated processes to produce quantitative models at scale
d. Work with the development team to build tools to accelerate and automate searching, data visualization, and advanced analytics
e. Determine interface design based on user’s requirements

Data Collection
a. Recommend types of data, and internal and external data sources needed to measure and/or predict outcomes
b. Integrate multiple data sets to build large and complex data sets
Data Processing: Build data flow channels and processing systems to extract, transform, load and integrate data from various sources