Senior Data Scientist

About the role

Carsome is looking for a Senior Data Scientist to lead the development and deployment of data-driven models that power pricing, forecasting, and AI-enabled decision-making across our platform. This role sits at the intersection of applied machine learning, business strategy, and engineering execution — you'll build models that directly influence how vehicles are priced and how auctions are optimized, while also taking ownership of ML operations and mentoring junior team members.

Key responsibilities

• Design, build, and maintain data models that support pricing strategy and demand/supply forecasting, ensuring outputs are accurate, explainable, and actionable for business stakeholders.
• Drive AI-related initiatives such as assortment optimization for auction dashboards, applying techniques like optimization algorithms, recommendation systems, or ranking models to improve auction outcomes and marketplace efficiency.
• Own the end-to-end lifecycle of models in production — versioning, monitoring, retraining triggers, and performance tracking — working closely with engineering to ensure models are reliable, scalable, and maintainable in a live environment.
• Translate business problems into structured data science projects, manage execution from scoping to deployment, and track outcomes against defined success metrics.
• Act as a technical go-to expert for junior data scientists — reviewing code and modeling approaches, providing guidance on best practices, and helping upskill the team on statistical rigor, ML techniques, and production-readiness.
• Partner with product, engineering, and business teams to align data science work with strategic priorities and ensure models are integrated into real workflows.

About you

• 3-6 years of experience in data science, applied machine learning, or a related quantitative field, ideally with exposure to pricing, forecasting, or marketplace/auction-type problems.
• Strong proficiency in Python (or R) and SQL, with hands-on experience building and deploying ML models in production.
• Practical experience with MLOps tools/practices (e.g., model monitoring, CI/CD for ML, experiment tracking — MLflow, Airflow, or similar).
• Solid grounding in statistical modeling, time-series forecasting, and/or optimization techniques.
• Experience with cloud platforms (AWS, GCP, or Azure) and distributed data processing tools (Spark, BigQuery, etc.) is a plus.
• Demonstrated ability to mentor or guide junior team members and communicate technical concepts to non-technical stakeholders.
• A track record of taking projects from idea to production, with measurable business impact.
• Bachelor's or Master's degree in Data Science, Computer Science, Statistics, Mathematics, Engineering or a related field.