Data Scientist

Job Description:

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.

Qualification & Experience

• 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.
• 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.