Senior Data Scientist

Accountabilities: Lead the development, implementation, maintenance, and continuous improvement of machine learning, LLM, and automation projects. Process, clean, transform, and analyze large-scale network and device telemetry datasets to create reliable inputs for modeling and decision-making. Design and implement feature engineering strategies that improve model accuracy, interpretability, robustness, and overall performance. Use Python and SQL to explore complex datasets, build analytical pipelines, and deliver reproducible, high-quality results. Develop and evaluate machine learning models for use cases such as anomaly detection, forecasting, and system optimization. Translate product and business requirements into clearly defined analytical problems, project scopes, and model development plans. Collaborate with engineering and product teams to integrate machine learning models into production systems and ensure they deliver measurable value. Balance experimentation with execution by prioritizing high-impact work and delivering reliable insights and models within demanding timelines. Contribute to MLOps workflows, including model deployment, validation, monitoring, and ongoing performance improvements. Identify opportunities to apply AI and automation techniques to improve operational efficiency, analytical capabilities, and connected-device experiences. Communicate findings, technical approaches, and model outcomes clearly to cross-functional stakeholders, supporting informed product and business decisions. Requirements Demonstrated experience leading, developing, and owning AI or data science projects from initial scoping through implementation and ongoing improvement. Strong practical knowledge of machine learning techniques, LLMs, and their application to real-world problems. Advanced Python programming skills, including experience with data science and machine learning libraries such as Pandas, NumPy, and Scikit-learn. Solid SQL skills, with the ability to query, aggregate, and join complex datasets efficiently. Proven experience transforming messy, high-volume data into structured, high-quality datasets suitable for analytical and predictive modeling. Strong understanding of feature extraction, selection, and transformation techniques, with an emphasis on improving model performance and interpretability. A solid foundation in statistics, probability, and applied machine learning, including model evaluation and validation. Experience translating business or product requirements into practical analytical solutions and delivering high-quality work under tight deadlines. Familiarity with deploying and monitoring machine learning solutions in production, including MLOps practices and workflows. Experience with AWS cloud services, particularly SageMaker and Amazon S3, or comparable cloud-based machine learning infrastructure. Strong communication, collaboration, and problem-solving skills, with the ability to work effectively across engineering, product, and data teams. A proactive, adaptable approach to technical challenges, with the ability to balance analytical rigor, experimentation, and efficient delivery. Benefits The opportunity to work on AI and machine learning solutions supporting connected environments at substantial global scale. Exposure to advanced technologies spanning network telemetry, smart spaces, machine learning, LLMs, and cloud-based platforms. The opportunity to lead and influence AI projects from development through production deployment and ongoing optimization. A collaborative, innovation-focused environment that encourages curiosity, creative problem-solving, and high-quality technical work. The opportunity to partner with major internet service providers and contribute to technology used across millions of locations and billions of managed devices. An inclusive, equal-opportunity workplace committed to fair employment practices and nondiscrimination. Salary, equity, healthcare, paid leave, and specific remote-work arrangements are not detailed in the provided job description and should be confirmed during the recruitment process.