*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.
