Data Scientist

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 automationResponsibilities:Quantitative Modelinga. Apply data analysis, data mining and data processing to present data clearlyb. Apply data mining techniques and develop software to investigate leads, identify patterns and regularities in datac. Develop data models based on advanced statistical modelling, data mining, and machine learning methodsBusiness Problem Framing: Manage the problem definition and hypothesis formulation processAnalytics Problem Framinga. Assist in the conceptualization of analytical projectsb. Break down problems to recompose them in ways that are solvablec. Partner with stakeholders to translate business problems into data science projectsd. Plan the development of analytical solutions from initial design through implementation, prototyping and testinge. Provide advice on the development of data analysis models based on project requirementsf. Provide subject matter expertise to stakeholders, and maintain an advanced knowledge of trends affecting the industryInsights Generationa. Assist with the development of actionable recommendationsb. Evaluate experiment outcomes to draw actionable conclusionsc. Guide stakeholders on how to act on findingsd. Synthesize findings into actionable insightse. Develop compelling, logically structured presentations including storytelling of research and/or analytics findingsProject Managementa. Assist in planning project timelines and resources needed, and setting work quality guidelinesb. Deliver projects in line with agreed standards, providing fit-for-purpose solutions within time, quality, and budget constraintsc. Prepare documentation to outline data sources, models and algorithms used and developedInformation Systems Implementationa. Develop prototypes and proof of concepts for the selected solutionsb. Assist with developing new data-discovery toolsc. Implement automated processes to produce quantitative models at scaled. Work with the development team to build tools to accelerate and automate searching, data visualization, and advanced analyticse. Determine interface design based on user’s requirementsData Collectiona. Recommend types of data, and internal and external data sources needed to measure and/or predict outcomesb. Integrate multiple data sets to build large and complex data setsData Processing: Build data flow channels and processing systems to extract, transform, load and integrate data from various sources