*This is kind of independent consultant role

Must-Have Requirements

Bachelor's / Master's / PhD degree in Business, IT, Mathematics, Science, Engineering, or related discipline.
Up to 4 years of relevant experience beyond first degree.
2–5 years of experience building production machine learning systems beyond notebooks and Kaggle competitions.
Strong Python programming skills.
Hands-on experience with ML frameworks such as scikit-learn, TensorFlow, or PyTorch.
Strong understanding of machine learning algorithms such as XGBoost, LightGBM, neural networks, and decision trees.
Experience in building optimization, predictive, and statistical models.
Experience with SQL and NoSQL databases.
Experience with data science toolkits, programming languages, and visualization tools.
Good applied statistical knowledge, especially in business and finance-related use cases.
Understanding of forecasting and regression challenges, including:
Lag feature leakage
Target leakage in cross-validation
High-cardinality categorical handling
Trade-offs between MAE, MAPE, and RMSE
Ability to interpret models using methods such as SHAP, partial dependence, and residual diagnostics.
Ability to explain technical results clearly to non-technical stakeholders.
Hands-on experience with Google Cloud Platform, especially:
BigQuery
Vertex AI
Experience with SQL feature pipelines and deployed serving endpoints.
Experience with Git-based workflows, CI/CD practices, and code review discipline.
Experience with code versioning, code review, and documentation.
Good working knowledge of productivity tools such as G Suite, Git, Jira, and Confluence.
Ability to work under pressure and adapt to change.
Ability to balance speed, reliability, and interpretability.

Nice-to-Have Requirements

Experience with deep learning for tabular and time-series problems such as:
TFT
N-BEATS
NeuralProphet
TabPFN
Chronos
Experience with AutoML tools such as PyCaret for rapid baselining.
Golang experience for performance-critical services.
Experience with LLM-based or agentic tooling such as:
LangGraph
MCP servers
Prompt engineering for structured outputs
Evaluation harnesses for LLM systems
Familiarity with design thinking methods.
Experience with open-source tools and libraries.
Strong monitoring discipline, including drift detection and model performance tracking in production.

Data scientist (AI Engineer)