3. Data, Science & AI

Evaluates time series models using expanding and sliding window cross-validation for robust performance assessment on unseen data.

Enables custom fine-tuning of TimeGPT for time series forecasting via Nixtla SDK, including dataset preparation, job monitoring, and model benchmarking.

Integrates external factors (holidays, weather, events) into TimeGPT forecasts to enhance prediction accuracy for demand and sales.

Executes mechanistic interpretability experiments using JSON specifications for family sweeps, itemsets, and validation.

Forecasts orderbook depth and spreads to optimize trade execution timing for large orders using predictive analytics.

Researches and summarizes Nixtla ecosystem updates and time-series forecasting content from web and GitHub, including release notes and papers.

Evaluates time series forecasting models using expanding and sliding window cross-validation for robust performance assessment.

Validates time series forecast quality by comparing current metrics (MASE, sMAPE) against historical benchmarks, detecting performance degradation.

Guides solving overdetermined algebraic, ODE, and PDE systems using REDUCE's CRACK package for Lie symmetry analysis and solution interpretation.

Computes Lie group symmetries of differential equations using ReLie in REDUCE, supporting conditional/contact/variational symmetries and Lie algebra utilities.

Enables multi-stage tool orchestration for large data processing without context window limitations, ideal for complex data pipeline construction.

AI-powered code indexer enabling semantic search, GraphRAG-based dependency analysis, and persistent code insight storage.

Builds, deploys, and manages production data pipelines with Nexla, supporting transformation, schema, access control, and monitoring via Python SDK or REST API.

Provides AI-driven coaching for Rocket League by analyzing local game replays to identify weaknesses, compare performance, and offer practice recommendations.

Synthesizes implementation plans from multi-agent debate reports using external AI review for consensus building.

Provides expert guidance for building Deep Reinforcement Learning agents and NLP systems using PyTorch 2.7+, TorchRL, and HuggingFace Transformers.

Provides industrial-strength NLP capabilities with spaCy 3.x for text processing, custom classifier training, and model deployment.

Provides machine learning workflows for molecular data, including dataset splitting, fingerprint selection, and scaffold-based train/test splits to avoid common pitfalls.

Computational materials science toolkit for crystal structures, phase diagrams, band structure, DOS, and Materials Project integration.

Provides modern RDKit workflows for molecular fingerprints, drawing, and property calculations in cheminformatics.

Provides a library for building and training graph neural networks (GNNs) for node classification, link prediction, and molecular property prediction.

Daily portfolio monitoring, performance analysis against benchmarks, and strategy execution using quantitative financial models for Indonesian investors.

Deploys AI models via NVIDIA NIM with OpenAI-compatible APIs, supporting self-hosted or cloud deployment for seamless integration.

Creates professional financial charts (Sankey, waterfall, bar, line) using Python and Plotly for data presentation and analysis.