Apply statistical analysis and machine learning to business problems: extract and transform data with SQL, build and deploy ML models (cloud preferred), monitor model performance, and communicate insights to technical and non-technical stakeholders while collaborating in agile cross-functional teams.
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 goal of GDI&A is to drive evidence-based decision making by providing insights from data. Applications for GDI&A include, but are not limited to, Connected Vehicle, Smart Mobility, Advanced Operations, Manufacturing, Supply chain, Logistics, and Warranty Analytics.
Responsibilities- Build an in-depth understanding of the business domain and data sources, demonstrating strong business acumen.
- Extract, analyze, and transform data using SQL for insights.
- Apply statistical methods and develop ML models to solve business problems.
- Design and implement analytical solutions, contributing to their deployment, ideally leveraging Cloud environments.
- Work closely and collaboratively with Product Owners, Product Managers, Software Engineers, and Data Engineers within an agile development environment.
- Integrate and operationalize ML models for real-world impact.
- Monitor the performance and impact of deployed models, iterating as needed.
- Present findings and recommendations effectively to both technical and non-technical audiences to inform and drive business decisions.
Qualifications:
- At least 3 years of relevant professional experience applying data science techniques to solve business problems. This includes demonstrated hands-on proficiency with SQL and Python.
- Bachelor's or Master's degree in a quantitative field (e.g., Statistics, Computer Science, Mathematics, Engineering, Economics).
- Hands-on experience in conducting statistical data analysis (EDA, forecasting, clustering, hypothesis testing, etc.) and applying machine learning techniques (Classification/Regression, NLP, time-series analysis, etc.).
Technical Skills:
- Proficiency in SQL, including the ability to write and optimize queries for data extraction and analysis.
- Proficiency in Python for data manipulation (Pandas, NumPy), statistical analysis, and implementing Machine Learning models (Scikit-learn, TensorFlow, PyTorch, etc.).
- Working knowledge in a Cloud environment (GCP, AWS, or Azure) is preferred for developing and deploying models.
- Experience with version control systems, particularly Git.
- Nice to have: Exposure to Generative AI / Large Language Models (LLMs).
Functional Skills:
- Proven ability to understand and formulate business problem statements.
- Ability to translate Business Problem statements into data science problems.
- Strong problem-solving ability, with the capacity to analyze complex issues and develop effective solutions.
- Excellent verbal and written communication skills, with a demonstrated ability to translate complex technical information and results into simple, understandable language for non-technical audiences.
- Strong business engagement skills, including the ability to build relationships, collaborate effectively with stakeholders, and contribute to data-driven decision-making.
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