The Global Data Insights and Analytics (GDI&A) Program and Launch Management Analytics team supports Ford’s Vehicle Launch Initiatives with analytical solutions to enable corporate targets to improve timing, quality, and cost metrics. We are looking for a manager to lead a team of data scientists, ML individual contributors, and software engineers in all phases of ongoing and future analytics projects, including problem formulation, data identification, model development, validation, and deployment. As a member of this dynamic team, you will have the opportunity to work with some of the brightest global subject matter experts in Vehicle Launch, Quality, Warranty, and the Voice of Customer who are transforming the automobile industry. The candidate should have great independence, exceptional collaboration and leadership skills, and self-discipline to guide original applied research and choose appropriate methodologies to solve related problems. We are especially excited about candidates with supervisory experience, passion for hands-on work, and a strong technical mindset who demonstrate a passion for developing data science teams and applying state-of-the-art solutions to novel and challenging problems.

Our team utilizes a diverse set of tools and methodologies from different technical fields including Machine Learning, Statistical Analysis, Simulation, Econometrics, Big Data platforms and more. We understand that you cannot be an expert in everything, and the set of techniques and technologies we are using today may change over time. Given the required qualifications, we are looking for candidates who are lifelong learners, driven, and curious.

Key Responsibilities:

• Use data science techniques to assemble and connect data across sources including engineering, launch, quality, and supplier to compile the history of a part as it traverses through the Ford design, purchasing, sourcing, assembly, and quality processes
• Create enablers to design common keys such as part numbers to link data across disparate data sources
• Engineer KPIs, fuse data, and leverage data science techniques to identify data patterns that are typically associated with high warranty and/or high repair 
• Create methods to associate downstream warranty with upstream data to create early warning indicators for optimizing vehicle launch cost, timing and quality metrics
• Translate results as job aid to design engineers and risk mitigation actions to high risk vehicle programs

Other Responsibilities:

• Work with business partners to collate and organize data related to design, launch, quality, and supplier towards improving the understanding of the vehicle launch process
• Lead a group of data scientists, data engineers, and software engineers, through exciting and challenging vehicle launch related projects
• Translate business needs into analytical problems, work hands-on along with the team, judge among candidate ML models, contribute towards best practices in model development, conduct code reviews, research state-of-the art techniques and apply them for the team and business needs.
• Develop and apply analytic solutions to address real-world automotive and Quality challenges
• Initiate and manage cross-functional projects, building relationships with business partners, and influencing decision makers
• Ability to work well under limited supervision and use good judgment to know when to update and seek guidance from leadership
• Communicate and present insights to business customers and executives
• Collaborate internally and externally to identify new and novel data sources and explore their potential use in developing actionable business results
• Explore emerging technologies and analytic solutions for use in quantitative model development
• Develop and sustain a highly performing team 

Basic Qualifications: 

• Master’s degree in Engineering, Data Science, Computer Science, Statistics, Industrial Engineering, or other data-related fields
• 5+ years of hands-on experience with application of supervised and unsupervised machine learning techniques in a quality related field
• High level understanding of software systems and processes using agile methodology
• Domain experience in having developed and applied ML models to improve quality metrics in an OEM
• 5+ years of experience working with a wide range of Data Science and Machine Learning frameworks such as Keras, TensorFlow,  PyTorch, Scikit-Learn, XGBoost, etc.
• 5+ years of experience in R & Python programming language, and DataRobot
• Demonstrated performance in working on developing analytical models and deploying them in GCP
• Familiarity with SQL, Spark, Hive, and other big data technologies
• Strong drive for results, sense of urgency, and attention to detail
• Strong verbal and written communication skills with the ability to present to cross functional levels of management
• Ability to work in a fast-paced environment with global resources under short response times and changing business needs

Preferred Qualifications: 

• PhD in Computer Science, Statistics, Industrial Engineering, or other data-related fields.
• 5+ years of experience with applications of ML models for anomaly detection, document classification, text clustering, topic modeling, sentiment analysis, etc.
• 5+ years of experience in applying a wide range of computationally intensive statistical methods, e.g. bootstrap inference, cross-validation to estimate prediction errors, Markov Chain Monte Carlo, etc. to real world problems.
• Familiarity with NLP, Deep Learning, neural network architectures including CNNs, RNNs, Embeddings, Transfer Learning, and Transformers.
• Experience working with NLP/NER systems and frameworks such as NLTK, SpaCy, Gensim, Stanford CoreNLP, OpenNLP, etc.