Description

Job Description

The Global Data Insights and Analytics (GDI&A) department at Ford Motors Company is looking for qualified people who can develop scalable solutions to complex real-world problems using Machine Learning, Big Data, Statistics, Econometrics, and Optimization.The candidate should possess the ability to 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), and generate/deliver insights to stakeholders. Candidates are expected to regularly refer to research papers and be at the cutting-edge with respect to algorithms, tools, and techniques. The role is that of an individual contributor; however, the candidate is expected to work in project teams of 2 to 3 people and interact with Business partners on regular basis.

Responsibilities

• Understand business requirements and analyze datasets to determine suitable approaches to meet analytic business needs and support data-driven decision-making by FCSD business team
• 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

Qualifications

• Master's degree in computer science, Operational research, Statistics, Applied mathematics, or in any other engineering discipline.
• Proficient in querying and analyzing large datasets using BigQuery on GCP. Strong Python skills for data wrangling and automation.
• 2+ 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.