Data Scientist

• 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 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).

• Commercial experience with various classical data science and Machine Learning (ML) models (e.g. decision trees, ensemble-based tree models, linear regression etc.).

• Solid knowledge of customer analytics concepts or advanced forecasting.

• Model hyperparameter tuning.

• Model validation frameworks.

• Experience with business requirements gathering, transforming them into technical plan, data processing, feature engineering, models evaluation.

• Previous experience in an analytical role supporting business will be a plus.

• Fluency in Python, basic working knowledge of SQL.

• Knowledge of specific DS/ML libraries.

• Solid experience in one of the cloud computing platforms (Databricks or GCP or Azure).

• Understanding of Causal machine learning.
• Experience in working with big data and distributed environments would be a plus.
• Commercial experience proven by multiple successful projects in the areas of forecasting would be a big plus.
• Experience with OOP in Python.
• Experience with MLOps.
• Familiarity with other languages R, Scala would be a plus.

General:

• Basic computer programming skills and familiarity with programming concepts.
• Strong business acumen.
• Experience with deep learning, reinforcement learning or other advanced modeling concepts in Classical Data Science problems.
• Ability to come up with creative solutions to address customer problems.