Understand business requirements and analyze datasets to determine suitable approaches to meet analytic business needs and support data-driven decision-making
Design and implement data analysis and ML models, hypotheses, algorithms and experiments to support data driven decision-making
Apply various analytics techniques like data mining, predictive modeling, prescriptive modeling, math, statistics, advanced analytics, machine learning models and algorithms, etc.; to analyze data and uncover meaningful patterns, relationships, and trends
Design efficient data loading, data augmentation and data analysis techniques to enhance the accuracy and robustness of data science and machine learning models, including scalable models suitable for automation
Research, study and stay updated in the domain of data science, machine learning, analytics tools and techniques etc.; and continuously identify avenues for enhancing analysis efficiency, accuracy and robustness
Job Requirements
Details:
Skills Required:
Data Mining, Data/Analytics dashboards, ALGORITHMS, Data/Analytics, Data Analysis, Data Science
Experience Required:
5+ experience in relevant field
Undertake statistical modeling, predictive analytics, and insight generation for the RAV platform
Perform deep dive analyses on RAV vehicles to detect early indicators and recommend actions to prevent/mitigate RAV
Execute quick turn studies for urgent business questions by mining, managing, and exploring large, high dimensional structured and unstructured datasets (e.g., RAV, quality, repair orders, repeat visits, parts orders/supply, service, warranty claims, recalls)
Visualize validation results, analyses, and predictions using Power BI to deliver clear qualitative and quantitative insights
Collaborate closely with architects, project managers, data engineers, software engineers, and other data scientists to test pipelines, validate outputs, and launch new platform capabilities and insights into production for maximum business value
Communicate, present, and interpret analytical results to non technical audiences, cross functional teams, and leadership on a regular basis