Data Scientist

The Strategy & Enterprise Analytics team within GDI&A supports analytical solutions and insights across the enterprise in areas like Sustainability, Environment & Safety Engineering (SE&SE), Enterprise Product Line Management (EPLM), and Battery Electric Vehicles (BEV). The Data Scientist discovers and curates new and existing data sources to create insights for the business. This is an exciting opportunity to utilize the latest tools and methods in Statistics, Big Data, and Data Science to solve a variety of business problems.

The role spans the full project lifecycle from analytic problem definition, through data gathering, analysis, model development, reporting/visualization development, testing, and operational deployment.

Expectations from the role/candidate:

• Potential candidates should possess a strong analytical mindset and be very comfortable with processing and manipulating large data sets in various formats
• Familiarity with analytics methods (descriptive/predictive/prescriptive) and tools (PowerBi, QlikSense,Plotly Dash) would be a definite plus
• Exposure to Cloud technologies (e.g., Google Cloud), including executing Machine Learning algorithms on Cloud would be definite plus
• Candidates should display interest and initiative in translating a business problem into an analytical problem and determining the appropriate analytical methods to be used.
• Examine, interpret, and report analytical results in both written reports and in oral presentations to varied audiences

Qualifications: 

• Master’s in Data science, Computer Science, Electrical Engineering, Applied math, Mechanical Engineering, Industrial Engineering, or related quantitative field
• BE / B-Tech in any related engineering discipline 
• Excellent problem-solving skills 
• Evidence of interest in analytics (courses taken, competitions participated in, etc.) 
• Strong communication skills and storytelling with data 
• Familiarity with any one of scripting (R/Python) languages or programming languages (e.g., C/C++/Java)
• Familiarity with BigQuery, SQL, and visualization tools (e.g., PowerBi, QlikSense)
• Exposure to Cloud based platforms like GCP
• Conceptual knowledge of predictive analytics techniques like Regression, Classification, Clustering, Tree-based algorithms etc., 
• Conceptual knowledge in one or more of the following domains: NLP, forecasting algorithms and deep learning etc.