Senior Data Scientist

Sigma Software; Warszawa; On-site; PLN 260,000 - 480,000; Full time

Job summary

Sigma Software is hiring a Senior Machine Learning Engineer in Warsaw to build and optimize production ML models for a live AdTech platform. You will work on censored bid-landscape modeling, real-time win probability, and constrained optimization across large-scale data ecosystems.

You will collaborate with senior engineers and data scientists, contributing to scalable ML platform design and end-to-end model deployment in a fast-moving, data-driven environment.

Qualifications

• 5+ years of experience in ML or DS with production-grade models evaluated against KPIs.
• Strong Python skills with numpy, pandas and scikit-learn.
• Strong SQL skills and handling large-scale datasets.
• Deep experience with XGBoost, LightGBM or CatBoost.
• Knowledge of regularization, calibration methods and categorical features.
• Probability, statistics, confidence intervals and power analysis fundamentals.
• Experience with feature engineering for structured/behavioral data.
• Hands-on with Spark or PySpark; familiar with experimentation and A/B testing.
• Experience with advanced validation, temporal splits and drift analysis.
• Knowledge of SHAP and permutation importance for explainability.
• Upper-Intermediate English or higher.

Responsibilities

• Build and improve censored bid-landscape models to estimate price distributions.
• Develop real-time win probability estimation models responsive to bid dynamics.
• Design hierarchical lift estimation models with confidence-based selection.
• Build conversion propensity models using sparse, delayed, or aggregated labels.
• Develop look-alike audience models using positive-unlabeled learning and embeddings.
• Implement advertiser-level calibration and monitor ranking/calibration quality.
• Design offline evaluation frameworks with IPS, doubly-robust estimators, and reweighting.
• Define exploration and propensity logging for reliable downstream evaluation.
• Develop constrained optimization for campaigns, pricing, and volume targets.
• Collaborate on data diagnostics and evidence-based model recommendations.
• Work with Customer team on post-launch tuning and validation.
• Prepare technical docs and knowledge transfer materials.
• Participate in architecture discussions for scalable ML platform design.

Skills

Python

numpy

pandas

scikit-learn

SQL

XGBoost

LightGBM

CatBoost

calibration methods

probability/ statistics

feature engineering

Spark / PySpark

experimentation frameworks

A/B testing

SHAP / permutation importance

English (Upper-Intermediate)

Tools

Spark

PySpark

Vertex AI

Job description

Join Sigma Software to help build advanced machine learning solutions for one of the large-scale players in the programmatic advertising ecosystem. We are looking for a Senior Machine Learning Engineer with strong production ML expertise and deep interest in real-time optimization systems, large-scale behavioral data, and AdTech challenges.

In this role, you will work with a dedicated Sigma Software team on a predictive modeling platform integrated with a live ad exchange processing hundreds of millions of auction requests daily. You will contribute to sophisticated ML solutions involving bid optimization, calibration, counterfactual evaluation, and constrained decision-making systems.

We as a company offer the opportunity to work on technically challenging products, collaborate with experienced engineers and data scientists, and make a direct impact on large-scale production systems.

CUSTOMER

Our Customer is a technology company operating supply-side infrastructure within the programmatic advertising ecosystem. The company manages a high-scale ad exchange platform and is investing in predictive decisioning capabilities to improve advertising performance, audience targeting, and campaign optimization through advanced machine learning technologies.

PROJECT

The project focuses on building a predictive modeling and optimization platform on top of a live ad exchange environment. The platform evaluates and filters advertising supply in real time, predicts high-performing audience contexts, builds look-alike audiences from small seed datasets, and optimizes campaign performance across multiple business objectives and operational constraints.

The team works on complex machine learning challenges including censored bid-landscape modeling, sparse and delayed conversion attribution, calibration systems, counterfactual evaluation, and constrained optimization models. The solution is designed for large-scale production use and close collaboration with the Customer’s internal data science organization.

Job Description

• Build and improve censored bid-landscape models to estimate clearing-price distributions from partially observed auction data
• Develop real-time win probability estimation models responsive to bid pricing dynamics
• Design and implement hierarchical lift estimation models with confidence-bound-based selection strategies
• Build conversion propensity models using sparse, delayed, and aggregate-only labels
• Develop look-alike audience modeling approaches using positive-unlabeled learning and embedding-based nearest-neighbor techniques
• Implement advertiser-level calibration strategies while independently monitoring ranking and calibration quality
• Design robust offline evaluation frameworks using inverse-propensity scoring, doubly-robust estimators, and importance reweighting
• Define exploration strategies and propensity logging approaches to ensure reliable downstream correction and evaluation
• Develop constrained optimization mechanisms for campaign objectives, pricing constraints, and volume targeting
• Contribute to data diagnostics, capability assessments, and evidence-based model recommendations
• Collaborate with the Customer team during post-launch tuning and performance validation cycles
• Prepare technical documentation and knowledge transfer materials for the Customer’s internal data science team
• Participate in architecture discussions and contribute to scalable ML platform design decisions

Qualifications

• 5+ years of experience in Machine Learning or Data Science with production-grade models measured against business KPIs
• Strong Python skills including numpy, pandas, and scikit-learn
• Strong SQL skills and experience working with large-scale datasets
• Deep practical experience with XGBoost, LightGBM, or CatBoost
• Strong understanding of regularization, calibration methods, and categorical feature handling
• Strong knowledge of probability, statistics, confidence intervals, and statistical power analysis
• Experience with feature engineering for structured and behavioral datasets
• Hands-on experience with Spark or PySpark
• Practical knowledge of experimentation frameworks and A/B testing methodologies
• Experience with advanced validation approaches including temporal splits, leakage detection, drift analysis, and slice-based metrics
• Understanding of explainability techniques such as SHAP and permutation importance
• Upper-Intermediate English level or higher

WILL BE A PLUS

• Experience in AdTech modeling including CTR/CVR prediction, bid-landscape modeling, audience segmentation, and RTB mechanics
• Experience working with sparse, delayed, or censored labels
• Knowledge of attribution modeling, survival analysis, and positive-unlabeled learning
• Practical experience with counterfactual and off-policy evaluation techniques
• Understanding of calibration methods including isotonic regression and Platt scaling
• Experience with hierarchical, empirical-Bayes, or partial-pooling models
• Knowledge of constrained or multi-objective optimization approaches
• Experience with uplift modeling and causal inference methods
• Experience with Vertex AI or similar managed ML training environments
• Publications, competitive modeling achievements, or open-source contributions related to Machine Learning or AdTech

Additional Information

PERSONAL PROFILE

• Strong analytical and problem-solving skills
• Ability to work effectively in a highly data-driven environment
• Strong communication and stakeholder management abilities
• Ability to explain complex modeling decisions to technical and non-technical audiences
• Proactive mindset with strong ownership mentality
• Attention to detail and scientific rigor in experimentation and evaluation