Data Scientist (Pricing)

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Description

Engage with the business objectives behind key tasks: pricing for B2B/B2C, forecasting and optimization of financial and operational metrics, incident prioritization, and improving customer journey efficiency (revenue growth, cost savings, business impact). Drive ML projects end-to-end: from problem definition and formalization to modeling, piloting, and presenting results. Collaborate with data engineers to collect the necessary datasets and assess implementation feasibility. Work with analysts on A/B test design: defining metrics, splits, and interpreting results. Develop models (classic ML and DL, time series, uplift modeling), conduct error analysis and performance evaluation. Prepare models and code for transfer to production infrastructure (deployment and releases handled by the technical team). Take ownership of model quality, monitor key metrics, and collaborate with the team on degradation issues and improvement plans. 3+ years of experience as a Data Scientist. Proven experience with end-to-end ML projects (ability to oversee the entire process). ). Hands-on experience with uplift modeling and time series tasks (with real business applications). Experience working with business stakeholders and understanding optimization problems: pricing, B2C, LTV, retention, AB testing, and more. Proficient in Python for DS/ML (writing clean, readable code for models and experiments). Confident SQL user (able to build datasets, write joins, and perform data analysis). Nice to have experience with MLflow/DVC, pipelines (Airflow or similar), anomaly detection/anti-fraud, deep learning (PyTorch/TensorFlow), and model monitoring.

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