Data Scientist

Do you believe data tells the real story? We do! Redefining mobility requires quality data, metrics and analytics, as well as insightful interpreters and analysts. That's where Global Data Insight & Analytics makes an impact. We advise leadership on business conditions, customer needs and the competitive landscape. With our support, key decision makers can act in meaningful, positive ways. Join us and use your data expertise and analytical skills to drive evidence-based, timely decision making.

In this position...

FCSA (Ford Customer Service Analytics) team, part of Sales and Services Data and Analytics (SSDA) department in Ford’s Global Data, Insight and Analytics (GDI&A) organization, is looking for a highly skilled Data Scientist. This role will be responsible to join the RAV (Re-Acquired Vehicle) analytics team supporting all stages of RAV analysis from problem formulation, model /analytical framework development, evaluation, to model deployment to help Ford and FCSD achieve RAV objectives. 

The FCSA RAV Analytics team applies advanced quantitative methods and systematic econometrics and AI/ML /LLM models to derive insights and solve a wide variety of challenging problems in various area of FCSD: forecasting, root cause analysis, quality action effectiveness on RAV, VOR/uptime, early quality detection…through analyzing vast amounts of data including RAV, quality, repair orders, repeat visits, parts order and supply, service, warranty claims, recall…etc. The deep-dive and advanced analytics/quantification analysis led by this supervisor will be instrumental in supporting FCSD's business objectives and transformation through data-driven decision-making.

What you'll do…

• Support regional CSM (Customer Satisfaction Model) framework design/development, model maintenance, model enhancement, and daily monitoring using Looker/Data Studio
• Support annual volume forecasting for key business metrics, taking into consideration factors such as brand, fuel type, launch type, new car sales, and macro environment, among other contributing sources
• Support root cause analysis by studying repair order and warranty claim history to derive and identify potential drivers/trends impacting service quality
• Conduct deep-dive analyses to help identify early indicators of emerging issues and uncover/provide insights to business teams to prevent or reduce negative outcomes
• Conduct quick turn-around studies to answer frequent/various/urgent business requests by mining, managing, and exploring a variety of data sources and extracting/deriving insights from structured and unstructured data sets that are often very large and high-dimensional
• Explore/apply the most relevant quantitative modeling techniques and tools in statistical analysis, econometrics, operations research/optimization, and AI/ML/LLM techniques to a variety of real-world business problems within FCSD, focusing on improving overall service quality
• Ensure overall quality of the data and solutions throughout the analytics development process
• Maintain and enhance existing quantitative models supporting FCSD daily decisions
• Integrate validation results and visualize analysis/prediction outcomes through visualization tools to provide qualitative/quantitative overviews
• Interact/collaborate effectively cross-functionally with architects, project managers, data engineers, software engineers, and other data scientists to test data and model pipelines, validate pipeline output, and ensure successful model launch into production to maximize business value delivery
• Independently communicate and present/interpret models and analytical results to business/non-technical stakeholders, cross-functional teams, and leadership on a daily basis

You'll have...

• Master’s degree in Statistics, Data Science, Econometrics, Engineering, Physics, or related quantitative field or a combination of education and equivalent work experience 
• 3+ years of hands-on experience on a broad range of analytical techniques including forecasting, regression, and AI/ML/LLM methodologies
• Strong understanding of machine learning algorithms, techniques, and frameworks, including deep learning, neural networks, and ensemble methods. 3+ years of proficient experience in training, evaluating, and fine-tuning a variety of machine learning models, ensuring optimal performance and reliability
• 3+ years of experience and proficient in programming languages such as Python for data manipulation, analysis, and model development
• 3+ years of experience in processing large, high-dimensional structured and unstructured data, including thorough data preprocessing, data cleaning, and feature engineering to prepare datasets for model training and evaluation

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