Job Description

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.

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.