The Data Scientist role is responsible for modelling complex problems,discovering insights and identifying process digital improvements or opportunities through the use of statistical, algorithmic, mining and visualization techniques.
In addition to advanced analytic skills, this role is also proficient at integrating and preparing large, varied datasets, architecting specialized database and computing environments, and communicating results.
Data Scientists work closely with clients, data stewards, project / program managers, and other IT teams to turn data into critical information and knowledge that can be used to make sound organizational decisions.
Other responsibilities include providing data that is congruent and reliable.
Need to be creative thinkers and propose innovative ways to look at problems by using data mining (the process of discovering new patterns from large datasets) approaches on the set of information available.
Need to validate the findings using an experimental and iterative approach. Will also need to be able to present back the findings to the business by exposing the assumptions and validation work in a way that can be easily understood by the business counterparts.
The Data Scientist will need a combination of business focus,strong analytical and problem solving skills and programming knowledge to be able to quickly cycle hypothesis through the discovery phase of the project.
Excellent written and communications skills to report back the findings in a clear,structured manner are required.
o Designs experiments, test hypotheses, and build models.
o Conducts advanced data analysis and highly complex designs algorithm.
o Applies advanced statistical and predictive modelling techniques to build, maintain, and improve on multiple real-time decision systems.
1. Business Requirements Leads discovery processes with stakeholders to identify the business requirements and the expected outcome.
Works with and alongside business analysts by suggesting other products of interest to the client.
Models and frames business scenarios that are meaningful and which impact on critical business processes and / or decisions.
2. Data Requirements and Architecture
Identifies what data is available and relevant, including internal and external data sources, leveraging new data collection processes such as smart meters and geo-
location information or social media.
Collaborates with subject matter experts to select the relevant sources of information.
Makes strategic recommendations on data collection, integration and retention
requirements incorporating business requirements and knowledge of best practices.
Develops innovative and effective approaches to solve client's analytics problems and
communicates results and methodologies.· Works in iterative processes with the client and validates findings.
Develops experimental design approaches to validate finding or test hypotheses.
Validates analysis using scenario modelling.
Identifies / creates the appropriate algorithm to discover patterns.
4. Qualification and Assurance
Assesses, with the business, opportunities to enhance the qualification and assurance of the information to strengthen the use case.
Defines the validity of the information, how long the information is meaningful, and what other information it is related to.
5. Access Management and Control
Works with the data steward to ensure that the information used in compliance with the regulatory and security policies in place.
Qualifies where information can stored or what information, external to the organization,may be used support of the use case.
Utilizes patterns and variations in the volume, speed and other characteristics of data supporting the initiative, the type of data (e.
g., images, text, clickstream or metering data) in predictive analysis.
7. Policies, Standards and Procedures
Develops usage and access control policies and systems in collaboration with the data steward.
Partners with the data stewards in continuous improvement processes impacting data quality in the context of the specific use case.
Recommends ongoing improvements to methods and algorithms that lead to findings,including new information.
8. Communications / Presentations
Presents and depicts the rationale of their findings in easy to understand terms for the business.
Presents back results that contradict common belief, if needed.
Communicates and works with business subject matter experts and organizational leadership.
9. Change Advocacy
Educates the organization both from IT and the business perspectives on new
approaches, such as testing hypotheses and statistical validation of results.
Helps the organization understand the principles and the math behind the process to drive organizational buy-in.
Provides business metrics for the overall project to show improvements (contribution to the improvement should be monitored initially and over multiple iterations).
Demonstrates the following scientist qualities : clarity, accuracy, precision, relevance,depth, breadth, logic, significance, and fairness.
Provides on-going tracking and monitoring of performance of decision systems and statistical models.
Leads the design and deployment of enhancements and fixes to systems as needed.