Data Scientist III

Position Summary What you'll do

Data Strategy Requires knowledge of understanding of business value and relevance of data and data enabled insights decisions Appropriate application and understanding of data ecosystem including Data Management Data Quality Standards and Data Governance Accessibility Storage and Scalability etc Understanding of the methods and applications that unlock the monetary value of data assets To understand articulate and apply principles of the defined strategy to routine business problems that involve a single function

Analytical Modeling Requires knowledge of feature relevance and selection Exploratory data analysis methods and techniques Advanced statistical methods and best practice advanced modelling techniques eg graphical models Bayesian inference basic level of NLP Vision neural networks SVM Random Forest etc Multivariate calculus Statistical models behind standard ML models Advanced excel techniques and Programming languages like RPython Basic classical optimization techniques eg Newton Rapson methods Gradient descent Numerical methods of optimization eg Linear Programming Integer Programming Quadratic Programming etc To select the analytical modeling technique most suitable for the structured complex data and develops custom analytical models Conduct exploratory data analysis activities for example basic statistical analysis hypothesis testing statistical inferences on available data Define and finalize features based on model responses and introduces new or revised features to enhance the analysis and outcomes Identify the dimensions of the experiment finalize the design test hypotheses and conduct the experiment Perform trend and cluster analysis on data to answer practical business problems and provide recommendations and key insights to the business Mentor and guide junior associates on basic modeling and analytics techniques to solve complex problems

Model Deployment and Scaling Requires knowledge of impact of variables and features on model performance understanding of servers model formats to store models To support efforts to ensure that analytical models and techniques used can be deployed into production Support evaluation of the analytical model Support the scalability and sustainability of analytical models

Code Development and Testing Requires knowledge of coding languages like SQL Java C Python and others Testing methods such as static dynamic software composition analysis manual penetration testing and others Business domain understanding To write code to develop the required solution and application features by using the recommended programming language and leveraging business technical and data requirements Test the code using the recommended testing approach

Tech Problem Formulation Requires knowledge of Analyticsbig data analytics automation techniques and methods Business understanding Precedence and use cases Business requirements and insights To translate coown business problems within ones discipline to data related or mathematical solutions Identify appropriate methodstools to be leveraged to provide a solution for the problem Share use cases and gives examples to demonstrate how the method would solve the business problem

Understanding Business Context Requires knowledge of Industry and environmental factors Common business vernacular Business practices across two or more domains such as product finance marketing sales technology business systems and human resources and indepth knowledge of related practices Directly relevant business metrics and business areas To Support the development of business cases and recommendations Drive delivery of project activity and tasks assigned by others Support process updates and changes Support under guidance in solving business issues

Data Source Identification Requires knowledge of Functional business domain and scenarios Categories of data and where it is held Business data requirements Database technologies and distributed data stores eg SQL No

SQL Data Quality Existing business systems and processes including the key drivers and measures of success To Understand the appropriate data set required to develop simple models by developing initial drafts Support the identification of the most suitable source for data Maintains awareness of data quality

Model Assessment and Validation Requires knowledge of model fit testing tuning and validation techniques eg Chi square ROC curve root mean square error etc Impact of variables and features on model performance To support model fit testing and statistical inferences to evaluate performance Assess the impact of variables and features on model performance

Data Visualization Requires knowledge of Visualization guidelines and best practices for complex data types Multiple data visualization tools for example Python R libraries GGplot Matplotlib Ploty Tableau Power

BI etc Advanced visualization techniques tools Multiple story plots and structures OABCDE Communication influencing…