Senior Data Scientist

Join an innovative technology team developing AI-powered solutions for connected homes, businesses, and enterprise environments at global scale. As a Senior Data Scientist, you will lead machine learning, large language model (LLM), and automation initiatives that transform large volumes of network and device telemetry into actionable insights. You will combine advanced analytical techniques with practical engineering to solve complex problems involving anomaly detection, forecasting, and optimization. Working closely with product, engineering, and data teams, you will help translate business needs into reliable models and production-ready solutions. You will contribute throughout the machine learning lifecycle, from data preparation and feature engineering to deployment and performance monitoring. This role offers the opportunity to shape intelligent connected-space experiences in a fast-moving, collaborative, and innovation-driven environment.

• 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.

• 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.