Mine and analyze data, applying statistical methods as necessary, pertaining to customers discovery and viewing experiences to identify critical product insights
Translate analytic insights into concrete, actionable recommendations for business or product improvement
Partner closely with product and engineering leaders throughout the lifecycle of project
Ensure that necessary data is captured; analytic needs are well-defined up front and coordinate the analytic needs
Drive efforts to enable product and engineering leaders to share your knowledge and insights through clear and concise communication, education, and data visualization
Requirements: Good knowledge of advanced statistical methods (automotive or manufacturing domain will be an added advantage)
Experience in creating statistical models and/or optimization frameworks for improving processes/products/profits Proactively develop new metrics and studies to quantify the value of different aspects of product
Should have independently handled a project technically and provided directions to the other Team Members
Experience in turning ideas into actionable designs
Able to persuade stakeholders and champion effective techniques through development
Ongoing technical authority role with our larger customers
Strong interpersonal and communication skills: ability to tell a clear, concise, actionable story with data, to folks across various levels of the company
Able to lead the project independently
Technical directions to junior in team, like to sort the respective task for responsible team members
Technical Skills: Expertise in following scripting languages Python, R, TensorFlow, Keras, Pytorch, OpenNLP, WordNet, NLTK, OpenCV Tech savy and willing to work with open-Source Tools Applying statistical and machine learning techniques, such as, mean-variance, k-means, nearest-neighbor, support vector, Bayesian time-series and network analysis to identify outliers, classify events or actors, and correlate anomalous sequences of events
Proven track record and experience with statistical modeling/data mining algorithms such as Multivariate Regression, Logistic Regression, clustering algorithms, Support Vector Machines, Decision Trees etc
Machine learning, deep learning, graph mining
DOE, Forecasting, Segmentation, Uncertainty Analysis etc

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