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.