Staff Data Scientist / Machine Learning Engineer

Qualifications and requirements

- 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. Strongly preferred:

- 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.

- Level of English – from Upper-Intermediate and above.

Job functions

- 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.

Desirable skills

- 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.

Conditions

- Experience collaborating with leaders in FinTech, Healthcare, Retail, and Telecom, including companies like Samsung, Siemens, and Johnson & Johnson.
- Opportunities to switch projects and develop expertise in various business domains.
- Flexible work conditions: fully remote, office-based, or hybrid options available.
- Professional, financial, and career growth guaranteed, with mentoring and adaptation systems for new employees.
- Potential to earn an additional $1,000 per month through company activities, included in the annual bonus.
- Access to a constantly updated corporate training portal with a comprehensive knowledge base.
- Engaging corporate culture with events like parties, game days, and snacks.
- Compensation for certifications (AWS, PMP, etc.).
- Referral program.
- Private health insurance and sports activity compensation.

Join us!

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