Carpenter Technology Corporation is looking to leverage data science, specifically Generative AI and Large Language Models (LLMs), to advance its digital transformation. The goal is to solve strategic and tactical business problems related to process improvement, yield improvement, and product development by analyzing complex data sets and building predictive models.
Requirements
- Proficiency in programming in Python, R, Julia, MATLAB, and SAS
- Knowledge of other programming languages and analysis tools (e.g. Scala, Java, Ruby, JavaScript, shell scripting)
- Strong familiarity with big data frameworks and tools (e.g. Hadoop, Spark, MapReduce, Hive, Pig, Luigi/Airflow, Kafka, Data streaming, NoSQL, SQL)
- Familiarity with cloud-based solutions (e.g. Azure, AWS EMR)
- Experience in consuming REST based API with JSON payload preferred
- Practical knowledge of analytical techniques and methodologies (e.g. machine learning, segmentation, mix and time series modeling, response modeling, lift modeling, experimental design, neural networks, data mining, optimization techniques)
- Understanding of data profiling and data cleansing techniques
Responsibilities
- Apply data science techniques to massive structured / unstructured data sets across multiple environments in order to discover patterns and solve strategic / tactical business problems – process improvement, yield improvement, and product development.
- Build statistical and machine learning models for detecting root causes in process and yield variability.
- Develop prescriptions with actionable and controllable recipes for critical process variables from model parameters with baseline performance and estimated performance upon implementation of model prescriptions.
- Design and conduct experiment for observational data to identify the factors associated with cost of poor quality and process variability – Randomized, Randomized Block, Latin Square, and Full factorial and apply appropriate general linear models such as Fixed effect, Random Effect, Mixed Effect Models to derive ANOVA, ANCOVA, MANOVA, and MANCOVA.
- Build process simulation model to identify optimal critical process path using both chaotic dynamic and stochastic process simulation such as Hidden Gauss-Markov and Monte-Carlo Simulation.
- Develop anomaly detection models such as iForest, Local Outlier Factor, GMM, one class SVM, etc. to identify anomalous behavior in critical process inputs for both batch and stream processing.
- Design, train, and fine-tune large language models such as GPT-4, BERT, or similar, for various applications and conduct experiments to improve model performance and efficiency.
Other
- MS/PhD preferred in computer science, mathematics, statistics, operations research, or related field.
- 3-6 years of experience in data science, analytics, and model building roles.
- Strong written and verbal communications skills, including with senior business leaders
- Experience working with remote colleagues and teams
- Natural curiosity and passion for empirical research and problem solving
