Data Scientist

Responsibilities:

• Design, develop, and deploy predictive and prescriptive models to support customer experience optimization, demand forecasting, operations efficiency, and customer behavior analysis.
• Apply a range of machine learning and AI techniques, including classical algorithms (e.g., Gradient Boosting, Random Forests, SVMs), deep learning architectures (e.g., LSTM, Transformers), and emerging AI approaches to solve complex business problems.
• Build, maintain, and optimize customer-facing and internal analytical models, such as recommendation systems, churn prediction, propensity modeling, customer segmentation, demand forecasting, matching, and assortment optimization.
• Perform feature engineering, clustering, statistical modeling, and time-series analysis using structured and unstructured data sources.
• Transition models from exploratory research environments (e.g., notebooks) into production-ready artifacts, including APIs, serialized models, and containerized solutions.
• Collaborate closely with engineering teams to ensure end-to-end MLOps integration, including CI/CD pipelines, model versioning, automated deployment, monitoring, and retraining strategies.
• Conduct advanced statistical analysis to identify trends, anomalies, and opportunities, delivering actionable insights through reports and analytical outputs.
• Support experimentation initiatives through A/B testing, KPI definition, causal inference, and measurement of business impact.
• Partner with product, engineering, analytics, and business stakeholders to identify and prioritize data science opportunities aligned with organizational goals.
• Communicate findings clearly, including assumptions, limitations, trade-offs, and recommendations, to both technical and non-technical audiences.
• Contribute to a strong data-driven culture by sharing knowledge, promoting best practices, mentoring peers, and advocating for responsible and ethical use of AI.

Requirements

• 3+ years of hands‑on experience in applied data science and machine learning, ideally within B2B, Retail, or Digital Commerce environments.
• Bachelor’s degree in Data Science, Computer Science, Statistics, Mathematics, Analytics, or a related field.
• Strong proficiency in Python, including experience with data science and ML libraries such as Pandas, NumPy, Scikit‑learn, and visualization tools.
• Advanced SQL skills, including the ability to write complex queries, procedures, and analytical transformations.
• Solid understanding of classical machine learning algorithms and their real-world business applications.
• Strong analytical, critical‑thinking, and problem‑solving abilities with a consistent focus on business impact.
• Professional working proficiency in English for global collaboration and technical communication.