Builds and validates predictive models of high risk/reward or ambiguous problems with no initial solution utilizing large scale data from multiple data sources and methodologies. Ensures adoption of the model and reviews other Data Scientists' models.
Serves as a thought leader and strategic partner to cross functional leaders in broad areas including but not limited to environmental/sustainability initiatives, new revenue model development & design of new mobility solutions
Uses data science and analytical techniques to create data-driven solutions for various business use-cases
Solves complex, strategic issues using data science methodologies across multiple business units or domains. Leads projects with external partners to develop a minimal viable product to meet those needs while resolving any issues that may arise.
Leads the analysis and mining of very large quantities of data with expertise in several domains to find patterns and insights utilizing statistical software
Adept with project management principles.
Writes Python code in on prem or cloud solutions within Bridgestone’s environment.
Leads the development data science standards for the department. Creates taxonomies, models, standards and processes around DS in the organization
Contributes to the organization's data strategy and influences the data roadmap. Leads the efforts to find new data sources or leveraging existing data sources and works with business partners to bring them into the data stack.
Interprets, communicates, and presents analytic results to C-Level executives and below
Consistently collaborates with fellow data scientists and frequently interacts with business partners, project managers, cross-functional teams, key stakeholders, and other domains to generate ideas/hypotheses, test hypotheses, build analytics capabilities and drive business value.
Mentors and assists in technically supervising less experienced Data Scientists. Guides the formulation of individual development plans for the Data Scientists. Leads best practice sharing opportunities and knowledge of industry trends and innovations in data science.
Analyzes D&A ecosystem to highlight areas for growth and M&A opportunities.
PhD degree in a quantitative field including but not limited to data science, physics, computer science, math, engineering, and statistics and 7+ years of data science experience (Or Master’s Degree with 10+ years’ experience).
Experience in two or more of manufacturing, retail operations, new product development, supply chain or marketing research (incl. digital media, subscription packages, market analysis).
Prior experience leading/working on large scope/macro initiatives or research such as those impacting broader sustainability/environmental and societal factors e.g., green procurement, reducing emissions, waste reduction
Able to work with large data sets from multiple data sources (text, speech, images, structured, unstructured).
10 years of programming experience in statistical software (for example Python, R, or SAS) and able to demonstrate proficiency at an expert level. At least 3 years’ experience with Python within the last 5 years.
Optimization and Simulation experience
Experience working in large and complex projects using common project management methodologies e.g., Agile, Waterfall, Six Sigma, Lean
Able to build or support the building of business cases related to data science related projects.
Enjoys working collaboratively with other data scientists and multiple stakeholders across the business unit and with external partners.
Confident in creating and communicating standards and best practices for data science projects and deliverables including setting project reviews and general quality assurance practices to review the work of more junior data scientists.
Adept at effectively solving complex problems by breaking them down into logical steps and communicating results in a concise way to Senior Executives and able to defend/debate the results of the analyses.
Intellectual curiosity, a passion for data and a results orientation.
Enjoys mentoring, training and coaching other data scientists and analysts
Expertise in Machine Learning in auto or related industries (incl. image, speech, pattern recognition & anomaly detection) – SciKit-Learn, TensorFlow or PyTorch, Pandas, NumPy
Expertise operating and deploying models in cloud environments e.g., AWS, Azure, collaborating with IT, Data Engineers, ML Engineers.
Experience with distributed computing (Spark/MLLib)
Experience in software development processes including Dev/QA/prod, release cycles, Continuous Improvement/ Continuous Development, source control, code reviews etc.
Experience in IoT and streaming data analytics
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