Sr. Principal Data Scientist / Machine Learning Engineer
1 month ago
Plano
Job Summary We're looking for an exceptionally skilled and experienced Sr. Principal Data Scientist / Machine Learning Engineer to lead and deliver high-impact AI/ML projects across Automotive domain. The ideal candidate will have a deep understanding of data science and machine learning tools, techniques, and algorithms, coupled with a proven track record of successfully leading projects from conception to deployment. This role demands strong client-facing communication skills and the ability to translate complex technical concepts into tangible business value. Key Responsibilities • Technical Leadership & Strategy:, • Serve as a primary technical expert and thought leader in Data Science and Machine Learning., • Define and drive the technical strategy for AI/ML initiatives, identifying high-value opportunities for optimization, predictive analytics, and process improvement across diverse use cases., • Architect and oversee the development of robust, scalable, and production-ready DS/ML models and solutions., • Stay at the forefront of the latest advancements in DS/ML, especially those applicable to various industries and large-scale data problems., • Project Leadership & Delivery:, • Lead end-to-end DS/ML projects, including requirements gathering, data exploration, model development, validation, deployment, and monitoring., • Define project scope, timelines, and deliverables, ensuring successful execution within budget and schedule constraints., • Mentor and guide junior and mid-level data scientists and ML engineers, fostering a culture of technical excellence and continuous learning., • Drive MLOps best practices for reliable and efficient model deployment and lifecycle management., • Client Management & Communication:, • Act as a trusted advisor to clients and internal stakeholders, understanding their business challenges and translating them into solvable DS/ML problems., • Effectively communicate complex analytical findings, model performance, and business recommendations to both technical and non-technical audiences., • Manage client expectations, present progress reports, and ensure stakeholder satisfaction., • Facilitate workshops and discovery sessions to identify new opportunities for AI/ML adoption., • Use Case Development & Problem Solving:, • Lead the identification, prioritization, and execution of complex AI/ML use cases that drive significant business impact., • Apply deep analytical skills to dissect complex problems, derive actionable insights from data, and design innovative solutions., • Develop and implement models for:, • Predictive Analytics: Forecasting, risk assessment, and anomaly detection., • Optimization: Improving efficiency, resource allocation, and decision-making., • Pattern Recognition: Identifying trends, segments, and relationships within large datasets., • Automation: Leveraging ML for intelligent process automation and enhanced operational efficiency., • Tool & Algorithm Proficiency:, • Demonstrated expertise in a wide range of DS/ML tools and platforms (e.g., Python, R, TensorFlow, PyTorch, scikit-learn, Spark, AWS Sagemaker, Azure ML)., • Deep understanding and practical application of various machine learning algorithms (e.g., supervised, unsupervised, reinforcement learning, deep learning, time series analysis, NLP, computer vision)., • Master's or Ph.D. in Data Science, Machine Learning, Computer Science, Engineering, Operations Research, Statistics, or a related quantitative field., • 8+ years of progressive experience in Data Science and Machine Learning roles, with at least 3-5 years in a leadership or principal-level capacity., • Demonstrated experience leading multiple end-to-end DS/ML projects successfully from concept to production., • Proven track record of managing client interactions, presenting technical solutions, and influencing strategic decisions., • Expertise in Python programming (NumPy, Pandas, Scikit-learn, Keras/TensorFlow/PyTorch)., • Strong understanding of statistical modeling, experimental design, and hypothesis testing., • Experience with cloud platforms (AWS, Azure, GCP) and MLOps principles., • Experience with real-time data processing and streaming analytics., • Knowledge of various industry verticals and their unique data challenges (e.g., finance, healthcare, retail, logistics, manufacturing)., • Experience with large-scale data architectures (e.g., data lakes, data warehouses, distributed computing)., • Publications or presentations in relevant fields.