Job Description
<h3>📋 Description</h3> • Design, build, and deploy machine learning models that power growth initiatives, such as customer segmentation, churn prediction, personalization, campaign optimization, and recommendation systems.
• Collaborate with data scientists to translate prototypes into scalable solutions.
• Collaborate with analysts and product managers to turn business questions into measurable ML solutions.
• Evaluate and select appropriate algorithms and models for specific tasks, ensuring scalability and efficiency.
• Develop and maintain data pipelines for model training, validation, and deployment.
• Develop scalable data and ML pipelines using best-in-class tools and practices (e.g., Airflow, Spark, MLflow).
• Conduct model testing, versioning, and documentation to ensure reproducibility and maintainability.
• Integrate ML models into product and marketing systems via APIs or batch/streaming services.
• Monitor model performance in production and implement feedback loops for continuous learning.
• Contribute to experimentation frameworks (e.g., A/B testing infrastructure) to evaluate ML-driven features.
• Ensure best practices in model validation, testing, and performance evaluation.
• Continuously improve existing systems by integrating new data sources and ML techniques.
• Maintain documentation, testing, and governance around models and datasets to ensure reliability and transparency.
• Work closely with stakeholders across various departments (e.g., Marketing, Sales, Product, R&D) to understand business needs and translate them into data science and machine learning solutions.
• Communicate complex technical concepts to non-technical stakeholders clearly and effectively.
• Stay up-to-date with the latest trends and advancements in machine learning and AI, and integrate new techniques into the team's workflow. <h3>🎯 Requirements</h3> • Bachelor's or Master’s degree in Computer Science, Machine Learning, Statistics, or related field.
• 2+ years of experience deploying ML models in production environments.
• Proficient in Python and ML libraries such as Scikit-learn, TensorFlow, PyTorch, or LightGBM and tools for model deployment (e.g., MLflow, Kubernetes, Docker, Metaflow).
• Experience with data pipeline tools (e.g., Airflow, dbt) and big data processing (e.g., Spark, Presto).
• Familiarity with cloud-based ML platforms (e.g., AWS SageMaker, Google Vertex AI).
• Proficient in programming languages such as Python, R, or Java.
• Solid understanding of statistical methods, machine learning algorithms, and deep learning techniques.
• Proven experience with big data technologies (e.g., Spark, Hadoop) and cloud platforms (e.g., AWS, GCP, Azure).
• Strong understanding of experimentation design and metrics relevant to growth (e.g., conversion rate, LTV).
• Comfortable working in a fast-paced, collaborative environment focused on measurable impact. <h3>🏖️ Benefits</h3> • competitive pay
• generous time off
• ample parental and wellness leave
• healthcare
• a retirement savings program
• much more