Carpenter Technology
Data Scientist II
Carpenter Technology Corporation is a leading producer and distributor of premium specialty alloys, including titanium alloys, nickel and cobalt based superalloys, stainless steels, alloy steels and tool steels. Carpenter Technology's high-performance materials and advanced process solutions are an integral part of critical applications used within the aerospace, transportation, medical and energy markets, among other markets. Building on its history of innovation, Carpenter Technology's wrought and powder technology capabilities support a range of next-generation products and manufacturing techniques, including novel magnetic materials and additive manufacturing. The data scientist trains, validates, and manages machine learning solutions, with a focus on Generative AI and Large Language Models (LLMs) to advance Carpenter Technology's digital transformation. Mines and analyzes complex and unstructured data sets using advanced statistical methods for use in data driven decision making. Performs research, analysis, and modeling on organizational data. Develops and applies algorithms or models to key business metrics with the goal of improving operations or answering business questions. Position is responsible for the entirety of ML model suite that address production quality issues for all the business lines. Builds ML simulation models to support R&D with product development. Primary responsibilities for the data scientist II include: Applying data science techniques to massive structured/unstructured data sets across multiple environments to discover patterns and solve strategic/tactical business problems process improvement, yield improvement, and product development. Building statistical and machine learning models for detecting root causes in process and yield variability. Developing prescriptions with actionable and controllable recipes for critical process variables from model parameters. Designing and conducting experiments for observational data to identify factors associated with cost of poor quality and process variability. Building process simulation models to identify optimal critical process paths. Developing anomaly detection models to identify anomalous behavior in critical process inputs. Designing, training, and fine-tuning large language models for various applications. Creating, testing, and refining prompts to enhance model outputs. Ensuring AI models adhere to ethical guidelines and address biases. Managing machine learning model life cycle through documentation, version control, and model audit. Communicating model findings clearly to stakeholders. Driving the collection, cleansing, processing, and analysis of data sources. Identifying and correcting errors, inconsistencies, and missing values in datasets. Developing and applying data validation rules to ensure data integrity. Reporting findings through appropriate outputs and visualizations. Staying current on analytics developments. Collaborating with cross-functional teams on data architecture and digital products. Performing all other duties and special projects as assigned. Required for the data scientist II: 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. Proficiency in programming in Python, R, Julia, MATLAB, and SAS. Knowledge of other programming languages and analysis tools. Strong familiarity with big data frameworks and tools. Familiarity with cloud-based solutions. Relevant certifications in AI/ML. Relevant certifications in data management or data quality are a plus. Experience in consuming REST based API with JSON payload preferred. Perform work under general supervision. Possess solid working knowledge of subject matter. Practical knowledge of analytical techniques and methodologies. Understanding of data profiling and data cleansing techniques. Strong written and verbal communications skills. Experience working with remote colleagues and teams. Natural curiosity and passion for empirical research and problem solving. Carpenter Technology Company offers a competitive salary and a comprehensive benefits package including life, medical, dental, vision, flexible spending accounts, disability coverage, 401k with company contributions as well as many other options to employees. Carpenter Technology Corporation's policy is to fully and effectively maintain a program of equal employment opportunity and nondiscrimination for all employees, to employ affirmative action for all protected classes, and to recruit and develop the best qualified persons available regardless of age, race, color, religion, sex, gender identity, sexual orientation, marital status, national origin, political affiliation or any other characteristic protected by law. The Company also will recruit, develop and provide opportunities for qualified persons with disabilities and protected veterans.
Carpenter Technology Corporation is a leading producer and distributor of premium specialty alloys, including titanium alloys, nickel and cobalt based superalloys, stainless steels, alloy steels and tool steels. Carpenter Technology's high-performance materials and advanced process solutions are an integral part of critical applications used within the aerospace, transportation, medical and energy markets, among other markets. Building on its history of innovation, Carpenter Technology's wrought and powder technology capabilities support a range of next-generation products and manufacturing techniques, including novel magnetic materials and additive manufacturing. The data scientist trains, validates, and manages machine learning solutions, with a focus on Generative AI and Large Language Models (LLMs) to advance Carpenter Technology's digital transformation. Mines and analyzes complex and unstructured data sets using advanced statistical methods for use in data driven decision making. Performs research, analysis, and modeling on organizational data. Develops and applies algorithms or models to key business metrics with the goal of improving operations or answering business questions. Position is responsible for the entirety of ML model suite that address production quality issues for all the business lines. Builds ML simulation models to support R&D with product development. Primary responsibilities for the data scientist II include: Applying data science techniques to massive structured/unstructured data sets across multiple environments to discover patterns and solve strategic/tactical business problems process improvement, yield improvement, and product development. Building statistical and machine learning models for detecting root causes in process and yield variability. Developing prescriptions with actionable and controllable recipes for critical process variables from model parameters. Designing and conducting experiments for observational data to identify factors associated with cost of poor quality and process variability. Building process simulation models to identify optimal critical process paths. Developing anomaly detection models to identify anomalous behavior in critical process inputs. Designing, training, and fine-tuning large language models for various applications. Creating, testing, and refining prompts to enhance model outputs. Ensuring AI models adhere to ethical guidelines and address biases. Managing machine learning model life cycle through documentation, version control, and model audit. Communicating model findings clearly to stakeholders. Driving the collection, cleansing, processing, and analysis of data sources. Identifying and correcting errors, inconsistencies, and missing values in datasets. Developing and applying data validation rules to ensure data integrity. Reporting findings through appropriate outputs and visualizations. Staying current on analytics developments. Collaborating with cross-functional teams on data architecture and digital products. Performing all other duties and special projects as assigned. Required for the data scientist II: 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. Proficiency in programming in Python, R, Julia, MATLAB, and SAS. Knowledge of other programming languages and analysis tools. Strong familiarity with big data frameworks and tools. Familiarity with cloud-based solutions. Relevant certifications in AI/ML. Relevant certifications in data management or data quality are a plus. Experience in consuming REST based API with JSON payload preferred. Perform work under general supervision. Possess solid working knowledge of subject matter. Practical knowledge of analytical techniques and methodologies. Understanding of data profiling and data cleansing techniques. Strong written and verbal communications skills. Experience working with remote colleagues and teams. Natural curiosity and passion for empirical research and problem solving. Carpenter Technology Company offers a competitive salary and a comprehensive benefits package including life, medical, dental, vision, flexible spending accounts, disability coverage, 401k with company contributions as well as many other options to employees. Carpenter Technology Corporation's policy is to fully and effectively maintain a program of equal employment opportunity and nondiscrimination for all employees, to employ affirmative action for all protected classes, and to recruit and develop the best qualified persons available regardless of age, race, color, religion, sex, gender identity, sexual orientation, marital status, national origin, political affiliation or any other characteristic protected by law. The Company also will recruit, develop and provide opportunities for qualified persons with disabilities and protected veterans.