The Global Data Insights and Analytics (GDI&A) department at Ford Motors Company is seeking qualified individuals to develop scalable solutions for complex real-world problems. By utilizing Machine Learning, Big Data, Statistics, Econometrics, and Optimization, the department aims to drive evidence-based decision-making across areas including Connected Vehicle, Smart Mobility, Advanced Operations, Manufacturing, Supply chain, Logistics, and Quality Analytics.
Responsibilities
- 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
- Translate a business problem into an analytical problem
- Identify the relevant data sets needed for addressing the analytical problem
- Recommend, implement, and validate the best suited analytical algorithm(s)
- Generate/deliver insights to stakeholders
- Regularly refer to research papers and be at the cutting-edge with respect to algorithms, tools, and techniques
- Work in project teams of 2 to 3 people and interact with Business partners on regular basis
Requirements
- Bachelor’s degree in Data science, computer science, Operational research, Statistics, Applied mathematics, or in any other engineering discipline.
- 3+ years of hands-on experience in Python programming for data analysis, machine learning, and with libraries such as NumPy, Pandas, Matplotlib, Scikit-learn, TensorFlow, PyTorch, NLTK, spaCy, and Gensim.
- 2+ years of experience with both supervised and unsupervised machine learning techniques.
- 2+ years of experience with data analysis and visualization using Python packages such as Pandas, NumPy, Matplotlib, Seaborn, or data visualization tools like Dash or QlikSense.
- 1+ years' experience in SQL programming language and relational databases.
- Excellent depth and breadth of knowledge in machine learning, data mining, and statistical modeling.
Nice to have
- An MS/PhD in Computer Science, Operational research, Statistics, Applied mathematics, or in any other engineering discipline (PhD strongly preferred).
- Experience working with Google Cloud Platform (GCP) services, leveraging its capabilities for ML model development and deployment.
- Experience with Git and GitHub for version control and collaboration.
- Besides Python, familiarity with one more additional programming language (e.g., C/C++/Java)
- Strong background and understanding of mathematical concepts relating to probabilistic models, conditional probability, numerical methods, linear algebra, neural network under the hood detail.
- Experience working with large language models such GPT-4, Google, Palm, Llama-2, etc.
- Excellent problem solving, communication, and data presentation skills.
Additional details
- The role is that of an individual contributor.
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