Data scientist in advertising optimization

Andersen is hiring a Staff Data Scientist / Machine Learning Engineer for a project developing an AI-powered advertising platform to predict customer lifetime value and optimize campaign performance. The project is focused on developing and enhancing an AI-powered advertising optimization platform that predicts customer lifetime value using machine learning models. It integrates predictive value signals with major advertising platforms to improve value-based bidding, optimize marketing budgets, and maximize campaign performance through accurate data calibration and automated signal delivery.

• Building and maintaining predictive Lifetime Value (pLTV) models using XGBoost, CatBoost, and BG/NBD within a tiered scoring architecture, routing users into appropriate model tiers based on data availability and observability rather than applying a one-size-fits-all regression approach.
• Designing, implementing, and maintaining model calibration layers to transform raw predictions into platform-ready values, incorporating haircut scalars, linear, logarithmic, and rank-based normalization, and asymmetric loss functions that penalize overestimating low-value customers more heavily than underestimating high-value customers.
• Owning feature engineering processes in BigQuery, developing and maintaining Gold-layer feature tables from cleaned Silver-layer data, including RFM metrics, behavioral embeddings, category affinity, and purchase velocity.
• Ensuring feature quality and reliability by evaluating data completeness, monitoring missing-value rates, and excluding features with high missing rates among new users rather than relying on imputation.
• Monitoring model drift and distribution shifts, implementing residual logging, comparing pre- and post-signal deployment cohorts, and identifying causal contamination in retraining datasets resulting from the model's influence on user acquisition.
• Maintaining and improving batch Union-Find identity resolution algorithms in BigQuery, connecting GA4 behavioral identifiers with Shopify transactional identifiers and optimizing identity match rates to improve signal quality.
• Developing and maintaining production-grade Python applications running on Google Cloud Run Jobs, orchestrating workflows through Apache Airflow, and ensuring reliable delivery of ML-generated signals to advertising platforms.
• Implementing and maintaining automated testing, CI/CD pipelines, idempotent processing, and dry-run safety mechanisms to ensure production stability, prevent costly errors, and protect client advertising performance.

• Hands-on experience in Machine Learning Engineering using Python, including developing, deploying, and maintaining production-grade ML models, for 5+ years.
• Proven experience with gradient boosting algorithms (XGBoost, CatBoost, or LightGBM), including hyperparameter tuning, feature importance analysis, and advanced model evaluation.
• Strong proficiency in SQL and BigQuery, including complex feature engineering queries, scheduled data transformations, and data validation.
• Hands-on experience deploying and maintaining ML workloads on Google Cloud Platform (GCP), including Cloud Run, Airflow/Composer, Vertex AI, and Pub/Sub.
• Solid understanding of model calibration, business-oriented loss functions, and the impact of prediction errors on downstream optimization systems.
• Practical experience integrating ML model outputs with external systems through APIs, such as advertising platforms, recommendation engines, pricing systems, or bidding systems.
• Level of English – from Upper-Intermediate and above.
• Будет плюсом: Experience in e-commerce or Direct-to-Consumer (DTC) environments, particularly customer lifetime value (LTV) prediction, purchase forecasting, churn modeling, or customer segmentation; Familiarity with probabilistic customer models (BG/NBD, Gamma-Gamma, or similar frameworks) or strong foundations in Bayesian statistics; Experience with advertising platform integrations, including Google Ads API (ConversionUploadService, Enhanced Conversions), Meta Conversions API (CAPI), or similar server-side event tracking systems; Knowledge of identity resolution and entity matching techniques, including graph-based identity stitching, probabilistic matching, or cross-device identity resolution; Experience with Terraform, CI/CD pipeline design, and Docker containerization for ML workloads; Knowledge of causal inference methods, A/B testing in advertising environments, or multi-armed bandit optimization; Familiarity with AI-assisted development tools and agentic coding workflows, such as Claude Code, Cursor, or similar solutions

• We give our employees an opportunity to attend and participate in the company’s BA meetups, as well as offer a compensation program for international professional certificates.
• There are many different ways to grow and develop at our company. You can improve as a tech specialist or a resource manager, and all your activities will be decently rewarded.
• You will have an opportunity to take on the role of a mentor and help a group of BA/SA beginners grow.
• Our employees have access to Andersen Knowledge Base, where they can take courses in a variety of areas: the art of negotiation, PreSale & Discovery, Agile essentials, product ownership, microservice architecture, API design, SQL, AWS, GDPR, UX/UI, people management, and more.