Kellogg Lead Data Scientist in MANCHESTER, United States
Grow with us as a Lead Data Scientist, supporting the UK-IT team by collaborating with the business to answer business questions by using data mining techniques, including pattern detection, graph analysis or statistical analysis – extracting knowledge from a dataset involving data management, data preprocessing, model and post-processing of found structure. He/she
The Lead Data Scientist will exercise their knowledge of descriptive and multivariate statistical techniques and applications, and database analysis tools and techniques to develop strategic insights to drive business goals.
WHAT WILL I BE DOING?
- May lead workstream project planning process from inception, technical design, development, testing and delivery of business intelligence solutions.
Works with the business stakeholders to identify the business requirements and the expected outcomes. Determines optimum requirements to meet business needs.
Model and frame business scenarios that are meaningful and which impact on critical business processes and/or decisions
Work in iterative processes with the business and validate findings. For example, validate discovered correlations across data with the business. Discovered findings can appear counter intuitive. Data scientists need to be able to expose the rationale to their findings in easy to understand terms for the business. Data scientists should be prepared to present back results that contradict common belief.
Understand and assess with the business the expected qualification and assurance of the information in support of the use case. This includes defining the validity of the information, how long the information is meaningful and what other information it is related to.
Develops and writes any business requirements and functional specifications for the design and implementation of reporting solutions.
Data Modeling (Design and Develop)
Analyze large, noisy datasets and identify meaningful patterns that provide actionable business results.
Identify what data is available and relevant, including internal and external data sources, leveraging new data collection processes such as geo-location information or social media.
Work with business subject matter experts to select the relevant sources of information. Evaluate the data sources on multiple criteria including cost.
Assess the volume of data supporting the initiative, the type of data it is (images, text, clickstream or metering data) and the speed or sudden variations in data collection.
Understand the use and ability to employ the appropriate algorithm to discover patterns. Examples include using statistical and data mining packages and dedicated frameworks such as Hadoop MapReduce.
Develop algorithms and predictive models to solve critical business problems
Develop and automate new enhanced imputation algorithms.
Create informative visualizations that intuitively display large amounts of data and/or complex relationships.
Suggest ongoing improvements to methods and algorithms that lead to findings, including new information.
Validates findings using experimental design approaches and by comparing appropriate samples
Coordinate data resource requirements between analytics team and engineering teams.
Work with product managers, engineers, and analytics team members to translate prototypes into production.
May lead or provide technical direction for the planning, designing, and execution of testing efforts.
Develops, executes and documents test plans.
Resolves issues based on test results.
Reviews test plans and monitors testing process to ensure that business results are adequately tested with minimal risk.
Standardized Reporting and Data Analytics
Work with the data steward to ensure that the information follows the compliance, access management and control policies of the organization. This includes qualifying where information can be stored or what information external to the organization may be used in support of the use case
Work with IT to support data collection, integration and retention requirements based on the input collected with the business. Examples here may include activities such as the definition of the blog collection process, definition and selection of the proper data infrastructure to perform the analysis, definition of the"perishability" of the data and the data archiving technology used to support the life cycle management of the data.
Collaborate with database and disaster recovery administrators to ensure effective protection and integrity of data assets.
Creates data mining and analytics architectures, coding standards, statistical reporting, and data analysis methodologies.
Designs and develops and optimizes enterprise-wide information “views” and complex reports.
Researches tools, frameworks and mechanisms for data analytics.
Provides input to the development of information quality metrics
Troubleshoots tools, systems, and software.
Recommends improvements of applications, as necessary and optimizes queries.
Performs report and query tuning to improve performance.
Supports and helps manage external resources, such as service providers and vendor field support.
Provides technical expertise as necessary..
Conduct research and make recommendations on big data infrastructure, database technologies, analytics tools, services, protocols, and standards in support of procurement and development efforts.
Drive the collection of new data and the refinement of existing data sources.
Interfaces with vendors to keep abreast of new technologies, pricing and customer applicability.
Participates in vendor evaluations.
Policy, Standards and Procedures
Assist in the development of data management policies and procedures.
Develop best practices for analytics instrumentation and experimentation.
Provides input to standards, policies and procedures for the form, structure and attributes of the business intelligence tools and systems.
Supports education of the organization both from IT and the business perspectives on these new approaches, such as testing hypotheses and statistical validation of results.
Designs and delivers end-user training and training materials.
Trains users to transform data into action-oriented information and to use that information correctly.
Provides guidance, training, and problem solving assistance to other team members.
Mentors less-experienced individuals.
WHAT DO I NEED TO DEMONSTRATE?
· Degree / MSc in Computer Science, Statistics, Mathematics or other related field. Or specialized training/certification. Or equivalent work experience.
· Experience working with SAS, SQL, Python, and ‘R’.
· Experience delivering visualisations through Tableau is preferable.
· Typically requires considerable related technical /business experience including a working knowledge in developing data management processes and systems.
· Experience in data analysis and data mining.
WHAT ELSE DO I NEED TO KNOW?
Work is performed in typical office environment including normal work activities.
Some international travel is required.
The role may require On-Call out of hours support.
This position requires regular attendance and punctuality in accordance with Company policies. Additionally, the ability to interact well with other employees and work overtime, as necessary, is required
Please note that we will only accept applications that are made to us via our Careers site.
If you experience any difficulty when applying please contact email@example.com.
Please note that the closing date may be subject to change. We will interview suitable candidates as they apply, so please don’t hesitate to take this opportunity to submit your application as soon as possible.
Title: Lead Data Scientist
Location: UK-North West England-Manchester
Requisition ID: INF000966
Job Function: Information Technology
Job Type: Professional
Closing Date: Sep 7, 2017, 3:59:00 PM
Relocation Assistance: No