Data & Analytics

Compute first-order difference between last and second-to-last rows per entity in panel data to capture recent trend direction and magnitude

Multi-round pseudo labeling with progressively confident test predictions merged into training plus OOF-based train label correction

Computes leak-free target encoding statistics (mean, std, min, max) using nested inner KFold within each outer CV fold, preventing target leakage that occurs with naive groupby-based encoding.

Splits train/validation using GroupShuffleSplit so that related samples (forks, families, sessions) never span both sets.

Fits a Gaussian Mixture Model on the joint feature-target space and samples synthetic data pairs to augment small tabular datasets.

Predefines minimal unsigned integer dtypes before CSV loading to cut DataFrame memory usage by 2-4x without any data loss.

Config-driven feature factory that generates groupby aggregation features from a declarative spec list, supporting count, mean, var, nunique, cumcount, and custom lambdas.

Model cumulative distribution via softmax output layer and CRPS loss — for probabilistic regression over discrete bins

Build user-level behavioral features (avg listing duration, relisting frequency, total items) by joining auxiliary activity tables that share user_id but not item_id with train/test

Precomputes item/content difficulty as historical mean accuracy, merged as a static feature for user-item prediction tasks.

Augments imbalanced tabular data by independently shuffling each feature column within a class, creating synthetic samples that preserve per-column marginal distributions.

Removes redundant features by iterating pairwise Pearson correlations and dropping one member of each pair exceeding a threshold.

Recommends items frequently purchased together with a customer's recent items using pre-computed pair dictionaries.

Streams large HDF5 files in fixed-size row chunks to compute summary statistics without loading the full dataset into memory.

Train a PyTorch autoencoder on time-series summary statistics to produce dense encoded features for downstream GBDT models

Detects sentinel anomaly values in numeric columns, creates a boolean flag feature, then replaces the sentinel with NaN for proper imputation.

Routes QA predictions through an answer-type classifier to emit boolean answers, extractive spans, or null based on type logits.

Samples N items uniformly by stride from a variable-length list, always preserving the first and last elements, to fit long sequences into a fixed token budget.

Two-stage stacking: transformer predictions plus hand-crafted features feed into a LightGBM meta-learner.

Distributes inference across multiple TPU cores using torch_xla, each core writing a CSV shard, then merges shards via groupby mean.

Computes word overlap ratio between two texts weighted by inverse corpus frequency, giving rare shared words more importance than common ones.

Enrich model input by mapping categorical codes to human-readable taxonomy descriptions and concatenating them as context for transformer models

Evaluates NER span predictions using bidirectional word-index overlap (>=50% both ways) to compute micro-F1 over predicted vs ground-truth spans.

Downsamples documents with no entity labels while keeping all positive samples, balancing class distribution in NER training without discarding entity-bearing examples.