Data Scientist

We are seeking a Data Scientist to develop and implement large-scale, high-impact analytical and end-to-end data science products. Your role will involve deriving insights from structured and unstructured data using scientific methods, algorithms, and systems. You will integrate an understanding of product functionality and customer perspectives to contextualize analytical problems. Utilizing data exploration techniques, you will identify new questions and opportunities within your problem domain, assessing the applicability and limitations of available data. Successful candidates will interpret analytical results, validate their methodologies, and continuously monitor, analyze, and iterate to improve solutions. Collaboration with peer partners will be essential to produce clear, compelling, and measurable outputs that drive product and service enhancements. The specific nature of these processes will vary within Ford Customer Service Division (FCSD) based on customer segments, technology in use, and product integration complexity.

Responsibilities

Strategic Thinking: Influence the company's strategic direction by identifying opportunities within extensive datasets and developing data-driven strategies for growth, cost savings, revenue, and profit. Requires hands-on experience with techniques such as user profiling, optimization, pattern mining/detection, classification/regression modeling, recommender systems, and deep learning models.

AI ML Modeling/Deployment: Possess extensive knowledge and experience with Data & Machine Learning Algorithms (e.g., Decision Trees, SVM, GBMs, Topic Modelling, Regression) and statistical libraries (e.g., TensorFlow, Keras, Pytorch). Experience in building metaheuristic algorithms for optimal data insights, multi-level forecasting algorithms for user trend prediction, and deploying ML models is crucial.

Data Engineering: Experience in creating ETL processes for sourcing and linking data in preparation for model/algorithm development. This includes domain expertise of datasets, evaluation of third-party data, and ensuring data quality.

Visualization: Create visualizations to connect disparate data points, identify patterns, and communicate insights effectively. This includes scientific and geographic visualization using tools like Power BI/QlikSense.

Qualifications

• Bachelor's Degree in a related field such as Data Science, Predictive Analytics, Statistics, Marketing Analytics, Applied Mathematics, or IT.
• 2+ years of experience applying statistical methods, including Principal Component Analysis, Correspondence Analysis, K-Means Cluster Analysis, Factor Analysis, Multivariate Analysis, Data Modeling, Linear and Non-linear Regression, and Partial Least Squares techniques.
• Experience working in cross-functional, agile product teams or analytic delivery teams.
• 2+ years of experience in the Automotive, Transportation, Digital Business, or Analytics Consulting sectors.
• 2+ years of experience in preparing and delivering executive presentations.
• 2+ years of experience serving as a senior technical lead, facilitating analytical/technical discussions on solution trade-offs for new feature implementations.

Preferred Requirements:

• Master's Degree in a related field such as Data Science, Predictive Analytics, Statistics, Marketing Analytics, Applied Mathematics, or IT.
• Expert-level proficiency in Advanced and Predictive Analytical Methods, including 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, and Linear or Mixed Integer Optimization.
• Expertise in open-source data science technologies such as Python, R, Spark, SQL, and Hadoop, acquired through academic coursework, online training, certifications, or project development.
• Demonstrated expertise in commercial tools such as Alteryx, Qlikview, and Tableau, acquired through training and/or project implementation.
• Experience building and deploying AI-driven applications using Google Cloud Platform (GCP), specifically Vertex AI and Gemini APIs.
• Experience collaborating on deploying and managing applications using containerization (Google Kubernetes Engine) and serverless solutions (Cloud Run, Cloud Functions).
• Experience implementing and maintaining DevSecOps standards through CI/CD pipelines (Google Cloud Build, Tekton) for continuous integration and delivery.
• Experience designing and developing scalable backend services using Python and FastAPI, and building responsive, accessible frontend interfaces with React and TypeScript.