Staff Data Scientist / Machine Learning Engineer

Andersen Lab; United States; Remote; USD 150,000 - 190,000; Full time

Benefits offered by this job

Private health insurance

Annual bonus

Company training portal

Referral program

Remote/hybrid options

Job summary

Ulvior is seeking an experienced Machine Learning Engineer in the United States to design, deploy, and maintain production-grade ML models using Python and gradient boosting.

You will own feature engineering in BigQuery, data validation, model calibration, and integration with external APIs. Remote/hybrid options are available with strong emphasis on scalable pipelines and cross-functional collaboration.

Qualifications

• Hands-on ML engineering in production environments.
• Proven experience with gradient boosting and hyperparameter tuning.
• Strong SQL and BigQuery data engineering capabilities.
• Experience deploying ML workloads on GCP (Vertex AI, Cloud Run, Pub/Sub).
• Understanding of model calibration and loss functions.
• Experience integrating ML outputs with APIs and external systems.

Responsibilities

• Develop, deploy, and maintain production-grade ML models in the cloud.
• Lead feature engineering in BigQuery and maintain Gold/Silver data layers.
• Monitor model drift, conduct A/B testing, and ensure data quality.
• Build scalable ML pipelines with Airflow, Vertex AI, and Cloud Run.
• Collaborate with advertising platforms and pricing/bidding systems.

Skills

Python ML

XGBoost

CatBoost

LightGBM

SQL

BigQuery

GCP

Vertex AI

Airflow

Cloud Run

CI/CD

A/B testing

Bayesian stats

Feature engineering

Identity resolution

Model calibration

LTV modeling

RFM analytics

Tools

Terraform

Docker

GitHub Actions

BigQuery ML

Job description

• 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.
• 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.
• 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.
• 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.

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