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Sr. Principal Data Scientist​/Machine Learning Engineer

Job in Plano, Collin County, Texas, 75086, USA
Listing for: Ascentt
Full Time position
Listed on 2025-12-20
Job specializations:
  • IT/Tech
    Machine Learning/ ML Engineer, AI Engineer, Data Scientist, Data Analyst
Job Description & How to Apply Below
Sr. Principal Data Scientist / Machine Learning Engineer

Ascentt

Plano

USD 130,

Sr. Principal Data Scientist / Machine Learning Engineer

Ascentt is building cutting-edge data analytics & AI/ML solutions for global automotive and manufacturing leaders. We turn enterprise data into real-time decisions using advanced machine learning and GenAI. Our team solves hard engineering problems at scale, with real-world industry impact. We’re hiring passionate builders to shape the future of industrial intelligence.

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.
  • 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, Tensor Flow, 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).
  • Proficiency in data manipulation, SQL, and working with large, complex datasets from various sources.

Qualifications

  • 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…
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