Data Scientist

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: 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.

• 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.

• 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.

Originally posted on Himalayas