As a Data Scientist, you will formulate approaches to solve problems using well-defined algorithms and data sources
You will incorporate an understanding of product functionality and customer perspective to provide context for those problems
You will use data exploration techniques to discover new questions or opportunities within your problem area and propose applicability and limitations of the data
Successful Data Scientists will interpret the results of their analysis, validate their approach, and learn to monitor, analyze, and iterate to continuously improve
You will engage with peer stakeholders to produce clear, compelling, actionable output that influences product and service improvements
The form of these processes will vary within Smart Mobility based on the customer area, the type of technology in use, and the complexity of product integration
Bachelor s Degree in related field (e.g., Data Science, Predictive Analytics, Statistics, Marketing Analytics, Applied Mathematics, IT)
3+ years of experience of statistical methods and their proper application e.g., principal component analysis, correspondence analysis, k-means cluster analysis, factor analysis, multi-variate analysis, data modeling, linear regressions, non-linear regressions, partial least squares techniques
3+ years of experience using statistical software (e.g., R, SAS, SPSS, STATA, Spark, Python-based tools)
Experience working in a cross-functional, agile product team environment/analytic delivery team.
2+ years of experience in Automotive, Transportation, Digital Business, or Analytics Consulting
3+ years executive presentation preparation and delivery
3+ years of experience acting as the senior technical lead helping facilitate analytical/technical discussions on solution tradeoffs for new feature implementations.
3+ years of experience of creating dashboards using commercial tools such as Qlikview, Looker and Tableau acquired through training and/or implementation on projects.
5+ years of experience using MS-Office (Excel, PowerPoint, OneNote)
Strategic Thinking: Able to influence the strategic direction of the company by identifying opportunities in large, rich data sets and creating and implementing data driven strategies that fuel growth including cost savings, revenue, and profit.
Modeling: Assessments, and evaluating impacts of missing/unusable data, design and select features, develop, and implement statistical/predictive models using edge algorithms on diverse sources of data and testing and validation of models.
Analytics: Utilize analytical applications like R, Python, Alteryx, ArcGIS to identify trends and relationships between different pieces of data, draw appropriate conclusions and translate analytical findings into business strategies or analytics software.
Data Engineering: Experience with creating ETL processes to source and link data in preparation for Model/Algorithm development. This includes domain expertise of data sets in the environment, third-party data evaluations, data quality
Visualization: Create visualizations to connect disparate data, find patterns and tell engaging stories. This includes both scientific visualization as well as geographic applications such as QlikSense.
