Role Description We are looking for a Machine Learning Engineer with strong statistical foundations and hands-on modeling experience to join the team. You will assist in building, maintaining, and improving predictive models across a range of business domains — customer retention, network performance, marketing, sales, and others as needs evolve. You will develop features from complex multi-source data and help maintain inherited models built by external partners. You will contribute to all phases of the modeling lifecycle, from data exploration through model delivery, under the direction of the team manager. This position will report directly to the team manager and work alongside data engineers and solutions architects. It can be filled remotely anywhere within the country. What You Will Do - Build and evaluate predictive models (logistic regression, XGBoost, ensemble methods) across customer retention, network, marketing, sales, and other business domains. - Engineer features from complex, multi-source enterprise data (billing systems, call center logs, CRM, network data) in Snowflake and Oracle. - Profile and investigate data quality issues — identify leakage, missingness patterns, join inconsistencies, and source-of-truth conflicts. - Maintain and improve inherited production models, including models built in Snowpark by external partners. - Perform SHAP-based model interpretability analysis and translate results into business-actionable insights. - Design and execute customer segmentation using clustering techniques on model outputs. - Write clear, thorough documentation of model logic, feature rationale, data assumptions, and known limitations. - Collaborate with the team to define target variables, population filters, and prediction windows grounded in statistical reasoning. Qualifications - 2–3 years of experience in a data science or applied statistics role (less experience considered for strong candidates). - Strong foundation in statistics: hypothesis testing, regression, classification, probability, bias-variance tradeoffs. - Proficiency in Python for data science (Pandas, scikit-learn, NumPy, Matplotlib/Seaborn). - Strong SQL skills, particularly with Snowflake or similar cloud data warehouses. - Experience with feature engineering from real-world, imperfect enterprise data — not just clean Kaggle datasets. - Ability to work independently and manage your own priorities with minimal oversight. - Clear written and verbal communication — you can explain a modeling decision to a non-technical stakeholder and document your work so others can follow it. Even Better - Experience with Snowpark (Python or SQL). - Exposure to Azure ML or similar cloud ML platforms. - Familiarity with MLOps concepts (model versioning, pipeline automation, drift monitoring). - Telecom or subscription-based industry experience. - Experience inheriting and maintaining models built by others. - Familiarity with Git-based workflows and version control for data science artifacts. Benefits - Medical, Dental, Vision Insurance Plans. - 401K Plan. - Health & Flexible Savings Account. - Life and AD&D, Spousal Life, Child Life Insurance Plans. - Educational Assistance Plan.

• Lead and support engineering managers and their teams building platform systems for personalized recommendations across Spotify • Set and evolve the technical vision for foundational capabilities, including targeting, serving, evaluation, and agent-driven systems • Make thoughtful decisions about what should become platform capabilities, ensuring teams can move quickly without unnecessary complexity • Guide incremental platform evolution, enabling continuous delivery rather than large, disruptive rewrites • Partner closely with product, data science, and engineering leaders to align priorities across multiple squads • Stay close to the technology when needed, contributing to architecture decisions and resolving complex production challenges • Encourage adoption of AI-assisted development tools and shape how teams use them effectively in day-to-day work • Hire, develop, and grow engineering leaders and individual contributors, building a strong and inclusive team culture

• Audit, secure, and optimize our existing cloud infrastructure (AWS) to ensure high availability, fault tolerance, and security for both training and production workloads. • Design and maintain scalable architectures for serving deep learning models (PyTorch/TensorFlow), optimizing for low latency and high throughput in handling complex infrastructure data. • Build and maintain automated pipelines for model testing, validation, deployment, and rollback. • Architect efficient, scalable compute environments for training complex computer vision and time-series models on large datasets. • Implement comprehensive monitoring for model drift, data quality, and system health, ensuring rapid response to performance degradation.

• Design and implement end-to-end ML systems, including data ingestion, feature processing, model training, and model serving • Architect and deploy scalable AI services supporting real-time and batch inference use cases • Build and maintain ML infrastructure across cloud environments (e.g., EC2, EKS, SageMaker, specialized inference hardware) • Develop and evolve MLOps platforms, including training pipelines, deployment workflows, feature stores, and model observability • Implement CI/CD and infrastructure-as-code patterns to automate model lifecycle management • Optimize model training and inference performance for cost, latency, and hardware efficiency • Monitor production ML systems for accuracy, reliability, and operational health • Partner cross-functionally with data engineering, architecture, governance, and security teams to ensure compliant and scalable solutions • Mentor team members on ML engineering, system design, and operational best practices • Contribute to special initiatives that advance AI platform maturity and engineering standards