ML Engineer - Tabular Data Experimentation

TANGOME • Warszawa, MZ, PL

Actively Hiring

Full-time Posted 8 days ago

Responsibilities

check_circle End-to-end ML on tabular and panel data: feature engineering, validation strategy including time-aware splits, gradient boosting and related methods, calibration, drift monitoring.
check_circle Designing A/B tests that answer real questions: hypothesis design, power and MDE, handling peeking and multiple testing, variance reduction (CUPED and similar), interference and network effects.
check_circle Extracting credible answers from offline data when online experimentation isn't feasible - DiD, synthetic control, IV, uplift modeling - and choosing the right method for the situation rather than the most fashionable one.
check_circle Knowing when offline evaluation is sufficient and when it isn't, and being willing to defend that judgment.
check_circle Designing evaluations for LLM-based components the team owns: offline metrics, online proxies, drift detection, power analysis, guardrail metrics - with the same rigor as classical ML.
check_circle Translating business questions into ML formulations: metrics, loss, constraints, the trade-offs that actually matter to the product.

5+ years in applied ML with real product impact, not Kaggle-only.
Deep working knowledge of tabular and panel data: temporal leakage, non-stationarity, the ways these problems differ from i.i.d.
Solid statistics and experimentation - comfortable designing a test from scratch, and equally comfortable pushing back when someone wants to "just look at the p-value."
Causal inference at the depth where you can pick a method, justify it, and explain what it doesn't tell you.
Production-grade Python - maintainable code, not just research notebooks.
SQL at production-analytics level; able to get to the right sample independently, including non-trivial joins, window functions, and reasoning about query plans.
A clear view of when an LLM is the right tool for a problem and when a GBDT or classical method is, and the ability to defend either choice with evaluations and cost.
Building or substantially reworking a production recommender - two-tower, GBDT ranking, rerankers, candidate generation and ranking at scale.
LLM-augmented retrieval and ranking: semantic retrieval, LLM rerankers, embedding-based candidate generation.
Causal ML tooling (DoubleML, EconML).
MLflow, W&B, feature stores.
LLM-as-judge methodology and a working understanding of its failure modes.
Prompt optimization as an empirical practice (DSPy-style or hand-rolled).

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