Senior Machine Learning Engineer

Position Description:

Data Scientist, Product & Program Definition Transformation We are seeking highly qualified and innovative Data Scientists to join our ambitious Product & Program Definition Transformation team. In this role, you will be central to developing the intelligent systems that power our move to a future-state PLM environment. You will apply your expertise to solve complex, real-world problems at the heart of our engineering and business processes, directly contributing to the development of our Intelligent Transition Model and a suite of AI-Powered Accelerators. Your primary responsibilities will include: Designing and building scalable AI/ML models to automate the decomposition and transformation of legacy artifacts (e.g., Program Direction Letters, Order Guides, BOMs) into the future-state data model. Developing analytics prototypes and accelerators that work on massive, diverse datasets to provide actionable insights for all project workstreams (e.g., VSM generation, relationship analysis, complexity metrics). Partnering with business and architecture teams to translate complex, often ambiguous, business logic into robust, automated solutions. Required Skills and Experience: 1. AI & Machine Learning Expertise: Generative AI & LLMs: Demonstrated, hands-on experience in applying Generative AI and Large Language Models to solve complex business problems. This includes expertise in advanced prompt engineering, model fine-tuning, and leveraging LLM APIs to automate the decomposition of complex business artifacts and unstructured text. Natural Language Processing (NLP) & Text Mining: Proven ability to apply NLP and text mining techniques to extract structured information, rules, and relationships from unstructured or semi-structured documents (e.g., requirement documents, "author notes," technical specifications). Classical Machine Learning: Strong foundation in applying traditional ML algorithms such as clustering, classification, decision trees, random forests, and support vector machines. 2. Data & Programming Skills: Data Processing and Wrangling: Extensive hands-on experience processing and wrangling both structured (e.g., BOMs, tabular data) and unstructured data from various formats, sizes, and storage mechanisms. Programming Languages: Strong proficiency in Python (including libraries like Pandas, NumPy) and flexibility to use other requisite languages and analytical tools as needed for the problem at hand. Big Data & Cloud Technologies (GCP): Experience working with large-scale datasets and cloud-based AI/ML platforms is essential. Proficiency with Google Cloud Platform (GCP) services such as Vertex AI, BigQuery, and Cloud Storage is highly preferred. 3. Mindset & Problem-Solving Approach: First Principles & Systems Thinking: A proven ability to deconstruct complex legacy processes using a first-principles approach, focusing on understanding the root cause of inefficiencies rather than just treating symptoms. Inquisitive and Tenacious Mindset: Excellent problem-solving skills, with a proven ability to challenge existing practices and ask "why" to uncover the logic behind established processes. Collaborative & Outcome-Oriented: A strong team player who can work effectively with cross-functional teams and is driven to build practical, scalable solutions that deliver tangible business value.

Skills Required

:LLM, GenAI, Machine Learnin

g
Skills Preferre

d:Python, Big Query, Google Cloud Platfo

rm
Experience Requir

ed:Senior Associate Exp: 3 to 5 years experience in relevant fi

eld
Experience Prefer

red:3+ years of experience in at least one of the following languages: Python, R, MATLAB, SAS Experience with GoogleCloud Platform (GCP) including VertexAI, BigQuery, DBT, NoSQL database and Hadoop Ecosy

stem
Additional Informat

ion :3+ years of hands-on experience in applying advanced machine learning and AI techniques, with demonstrated proficiency in the following areas: 1. Generative AI (GenAI) and Large Language Models (LLMs): Proven expertise in leveraging GenAI, with hands-on experience in prompt engineering, model fine-tuning, and using LLM APIs for complex data processing, text mining, and automation. 2. Deep Learning: Proficiency in deep learning principles, including the design and implementation of neural networks, reinforcement learning, and an understanding of transformer architectures. 3. Classical Machine Learning: Strong foundation in traditional ML algorithms such as clustering, classification, decision trees, random forests, and support vector machines for predictive modeling and data anal