Job Description

Responsibilities & Key Deliverables

Core Responsibilities

Design, implement, and deploy robust AI and machine learning models focused on: + Predictive pricing and accurate valuation of pre-owned vehicles based on comprehensive data analysis.
+ Condition assessment of vehicles through advanced sensor data interpretation and inspection report analysis.
Develop and maintain scalable, end-to-end ML pipelines primarily using Python, ensuring efficient data processing and model execution. Handle diverse automotive datasets, including structured data, unstructured textual information, and time-series sensor inputs, facilitating comprehensive model training. Collaborate effectively with diverse teams such as product management, data engineering, and business units to drive solutions from concept to production environments. Oversee continual model performance monitoring, retrain models using up-to-date data, and optimize algorithms to maintain prediction accuracy over time. Create and maintain interactive dashboards and visualizations to communicate analytical insights clearly to stakeholders across technical and non-technical backgrounds. Incorporate best practices in MLOps, including model versioning, automated testing, and deployment using containerization technologies.

Essential Skills

Proficiency in Python programming with solid experience in machine learning frameworks and libraries, such as scikit-learn, XGBoost, CatBoost, TensorFlow, and PyTorch. Strong command over data processing and visualization libraries including Pandas, NumPy, Matplotlib, and Seaborn to handle complex datasets and present insights. Hands-on experience deploying machine learning models using modern tools such as Docker, FastAPI, and MLflow to ensure seamless integration and scalability. Familiarity with cloud computing platforms, including AWS, GCP, or Azure, coupled with a solid understanding of MLOps workflows and automation. Expertise in data preprocessing, feature selection and engineering, as well as rigorous model evaluation techniques. Demonstrated capability to take full ownership of projects, working independently while driving collaboration with cross-functional teams.

Experience

Minimum of 3 to 5 years of professional experience in data science roles, specializing in machine learning model development and deployment. Experience within automotive analytics, mobility services, or retail pricing sectors is highly valued, reflecting an understanding of domain-specific challenges and datasets. Knowledge of emerging technologies such as Generative AI, natural language processing (NLP), and deep learning methods is an advantage, enhancing model sophistication and applicability. Proven track record of successfully delivering production-level AI/ML projects that impact business outcomes significantly. Exposure to handling large, multi-modal datasets and improving model robustness in dynamic environments.

Qualifications

Bachelor's degree in Computer Science, Data Science, Statistics, Mathematics, or a related technical discipline is required. A Master's or doctoral degree in a relevant field is considered an asset and may substitute for some experience requirements. Strong academic foundation in machine learning, statistical modeling, and algorithm development essential for this role. Commitment to continuous learning and staying current with advancements in AI and machine learning technologies.

Job Segment:

Scientific, Engineer, Automotive, Engineering