Data Scientist and AI Engineer

As a Data Scientist, you are responsible for developing large scale & high impact analytical / end to end data science products to understand & derive insights from structured & unstructured data using scientific methods, processes, algorithms and systems.. You will incorporate an understanding of product functionality and customer perspective to provide context for those problems. You will use data exploration techniques to discover new questions or opportunities within your problem area and propose applicability and limitations of the data. Successful Data Scientists will interpret the results of their analysis, validate their approach, and learn to monitor, analyze, and iterate to continuously improve. You will engage with peer partners to produce clear, compelling, measurable output that influence product and service improvements. The form of this process will vary within Ford Customer Service Division(FCSD) based on the customer area, the type of technology in use, and the complexity of product integration.

Strategic Thinking: Able to influence the strategic direction of the company by identifying opportunities in large, rich data sets and crafting and implementing data driven strategies that fuel growth including cost savings, revenue and profit. They should have hands on experience on techniques such as user profiling, optimization, pattern mining / detection, classification / regression modelling, recommender systems & deep learning models etc.

 

AI ML Modeling/Deployment: They should have extensive knowledge & experience of Data & Machine Learning Algorithms (such as Decision Trees, SVM, GBMs, Topic Modelling, Regression) and statistical libraries (such as TensorFlow, Keras, Pytorch, etc.). And also build metaheuristic algorithms to derive optimal data insights, multi level forecasting algorithms to predict user trends along with experience of deploying ML models

 

Data Engineering: Experience with creating ETL processes to source and link data in preparation for Model/Algorithm development. This includes domain expertise of data sets in the environment, third-party data evaluations, data quality

 

Visualization: Create visualizations to connect disparate data, find patterns and tell engaging stories. This includes both scientific visualization as well as geographic using applications such as power BI/QlikSense.

• Bachelor’s Degree in related field (e.g. Data Science, Predictive Analytics, Statistics, Marketing Analytics, Applied Mathematics, or IT)
• 2+ years of experience of statistical methods and their proper application e.g., principle component analysis, correspondence analysis, k-means cluster analysis, factor analysis, multi-variate analysis, data modeling, linear regressions, non-linear regressions, partial least squares techniques
• Experience working in a cross-functional, agile product team environment/analytic delivery team. 
• 2+ years of experience in Automotive, Transportation, Digital Business, or Analytics Consulting
• 2+ years executive presentation preparation and delivery
• 2+ years of experience acting as the senior technical lead helping facilitate analytical/technical discussions on solution tradeoffs for new feature implementations.

Our preferred requirements: 

• Master’s Degree in related field (e.g., Data Science, Predictive Analytics, Statistics, Marketing Analytics, Applied Mathematics, IT) 
• Expert level of Advanced and Predictive Analytical Methods e.g., Simulation, Design of Experiments, Genetic Algorithms, Ensemble Methods, Naïve Bayes, Neural Networks, Support Vector Machines, Interaction Effects, Importance modeling, machine learning, regression, image processing, natural language processing, control charting, linear or mixed integer optimization 
• Expertise in open source data science technologies such as Python, R, Spark, SQL, Hadoop, etc. acquired through college course work, online training and certification or project development 
• Demonstrated expertise in commercial tools such as Alteryx, Qlikview, and Tableau acquired through training and/or implementation on projects 
• Build and deploy AI-driven applications by leveraging Google Cloud Platform (GCP), especially Vertex AI and the Gemini APIs.
• Collaborate on deploying and managing applications using containerization (Google Kubernetes Engine) and serverless solutions (Cloud Run, Cloud Functions).
• Implement and maintain DevSecOps standards through CI/CD pipelines (Google Cloud Build, Tekton) to ensure smooth, continuous integration and delivery.
• Design and develop scalable backend services using Python and FastAPI, and build responsive, accessible frontend interfaces with React and TypeScript.