Senior Data Scientist

Responsibilities -Lead end‑to‑end data science initiatives, from problem definition and data exploration to model development, deployment, and performance monitoring. -Partner with product, engineering, and business teams to identify high‑impact opportunities and translate them into data‑driven solutions. -Build advanced machine learning models, including predictive modeling, optimization, recommendation systems, NLP, or deep learning solutions depending on business needs. -Develop scalable data pipelines and analytical frameworks to support experimentation, model training, and real‑time inference. -Conduct rigorous statistical analysis, A/B testing, and causal inference to evaluate product changes and business strategies. -Communicate insights, model results, and recommendations to technical and non‑technical stakeholders with clarity and influence. -Mentor junior data scientists and contribute to best practices in modeling, experimentation, and data quality. -Stay current with emerging ML techniques, tools, and industry trends to guide long‑term data science strategy.

Qualifications -Bachelor’s degree or above in Computer Science, Statistics, Mathematics, Data Science, or a related field; advanced degrees preferred. -Extensive experience in data science or machine learning roles, with a strong track record of delivering production‑level models. -Proficiency in Python and common data science libraries (Pandas, NumPy, Scikit‑learn, PyTorch, TensorFlow). -Strong foundation in statistics, machine learning theory, and applied modeling. -Experience working with large‑scale datasets and distributed computing frameworks (Spark, Ray, Hadoop). -Familiarity with modern data infrastructure, cloud platforms, and MLOps practices. -Ability to translate complex analytical findings into actionable business insights. -Excellent communication skills and experience collaborating across cross‑functional teams. Preferred Qualifications -Experience with deep learning, NLP, recommendation systems, or time‑series forecasting. -Background in experimentation platforms, causal inference, or Bayesian modeling. -Experience deploying ML models in production environments. -Familiarity with data visualization tools (Tableau, Power BI, Looker) or dashboard development. -Contributions to open‑source projects, publications, or conference presentations.

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Cassie Lin