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. 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). 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 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. Excellent communication, interpersonal, and presentation skills.

Preferred Qualifications

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.

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