Data Scientist – Advanced Analytics & Machine Learning

About the role

As a Data Scientist you will join a global, high‑performing team to design, build and operationalise advanced data science and machine learning solutions that drive customer experience, demand forecasting, operations optimisation and revenue growth across B2B and digital commerce environments.

Key responsibilities

• Design, develop and deploy predictive and prescriptive models for customer experience optimisation, demand forecasting, operations efficiency and behaviour analysis.
• Apply machine‑learning techniques such as Gradient Boosting, Random Forest, SVM, LSTM and Transformers to solve complex business problems.
• Build and maintain recommendation systems, churn prediction, propensity modelling, segmentation, matching and assortment optimisation.
• Perform feature engineering, clustering, statistical modelling and time‑series analysis on structured and unstructured data.
• Transition models from notebooks to production‑ready artifacts, including APIs, serialized models and containerised solutions.
• Collaborate with engineering teams to implement end‑to‑end MLOps, CI/CD pipelines, model versioning, automated deployment, monitoring and retraining.
• Conduct advanced statistical analysis, A/B testing, causal inference and KPI definition to deliver actionable insights.
• Partner with product, engineering, analytics and business stakeholders to prioritise data‑science opportunities.

Required profile

• 3+ years of hands‑on experience in applied data science and machine learning, preferably in B2B, retail or digital commerce.
• Bachelor’s degree in Data Science, Computer Science, Statistics, Mathematics, Analytics or a related field.
• Strong communication skills to convey findings to technical and non‑technical audiences.

Required skills

• Python programming
• Pandas, NumPy, Scikit‑learn
• Advanced SQL
• Machine‑learning algorithms: Gradient Boosting, Random Forest, SVM
• Deep‑learning frameworks: LSTM, Transformers
• MLOps practices, CI/CD pipelines, API development
• Containerisation (e.g., Docker)