Are you a data-driven problem solver with a passion for machine learning and predictive analytics? Do you thrive on building models that drive real business impact, optimizing processes, and uncovering insights from complex datasets? Are you both detail-oriented and capable of seeing the bigger picture? If you’re looking to apply your ML expertise in a fast-growing and dynamic company, we want to hear from you!

At eCarsTrade, we value innovation, analytical thinking, and a results-driven mindset. In return, we offer flexibility, competitive incentives, and an environment for professional growth.

Responsibilities

• Develop and maintain predictive models to estimate used car market prices, customer willingness-to-pay, supplier acceptance prices, lead time etc...
• Design, implement, and maintain analytical and ML microservices using Python.
• Collaborate with cross-functional teams (Product, Sales, and Engineering) to identify business needs and model opportunities.
• Identify and correct inaccuracies in car data (e.g., missing features, incorrect attributes).
• Build recommendation engines to suggest optimal auction strategies and vehicle pricing.

Qualifications

• Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, or a related field.
• 5+ years of experience building and deploying end-to-end ML models and analytical solutions.
• Ability to work independently and manage multiple projects with minimal supervision.
• Experience building and maintaining microservices (e.g., using Flask, FastAPI, or similar frameworks).
• Strong Python coding skills, including experience with libraries such as Pandas, NumPy, SciPy, scikit-learn, and possibly PySpark.
• Deep understanding of regression, classification, time-series forecasting, and clustering methods.
• Strong communication and presentation skills for both technical and non-technical audiences.

Nice-to-Have

• Experience in automotive or auction-based industries.
• Experience with ML lifecycle and MLOps (model versioning, CI/CD pipelines for ML).

Please send your CV to [email protected]