Our team is delivering business solutions with Machine Learning and Data Science, often turning them into scalable platforms including non-trivial and innovative solutions in the space of Forecasting and Customer Analytics, often leveraging cutting-edge Causality frameworks. Example projects in this area include Next Best Offer/Action, propensity modeling, churn modeling, forecasting of demand and sales, revenue growth management, etc. This role requires close collaboration with business stakeholders and the ability to understand their problems and translate them into machine learning ones.

Tasks:

- Work on end-to-end classification and forecasting use cases: problem framing, data preparation, model development, evaluation, and basic deployment support (e.g., demand forecasting, churn prediction).

- Explore and clean data; perform EDA to understand data and flag data quality issues.

- Engineer features for tabular and time-series data.

- Train, validate, and tune standard ML models (e.g., logistic regression, tree-based models, gradient boosting, simple neural nets, classical time-series models).

- Evaluate models with appropriate metrics that have an impact on business KPIs.

- Build clear visualizations and concise reports to present model results and insights to business stakeholders.

- Collaborate with data engineers and AI engineers to bring models into production (batch scoring, APIs, models monitoring, dashboards).

- Document data sources, modeling assumptions, and experiment results in a reproducible way (notebooks, reports, wikis).

- Business understanding and translating problems into technical goals by defining success metrics, auditing data feasibility, and aligning stakeholder expectations.

- Pre-sales activities (at senior consultant level).