Role overview
- Develop and optimize ETL pipelines, ensuring high-quality, reliable data
- Design and conduct statistical studies and data analysis to evaluate the impact of internally adopted AI tools, research, and engineering results and to create interpretable insights and make data-driven decisions
- Curate and maintain datasets to support the development, evaluation, and deployment of AI models
- Provide technical leadership, mentorship, and guidance to the AI team and internal research projects, fostering a culture of innovation and excellence
- Partner with machine learning engineers, product managers, and executives to translate data insights into tangible business and product improvements
- Develop scalable algorithms and automated data processing frameworks to optimize analytics workflows
What you'll work on
- PhD or MS in Computer Science, Data Science, Statistics or a related quantitative field with scientific background and with 5+ years of relevant experience
- Strong expertise in data science, Bayesian modeling, probabilistic programming, and uncertainty quantification
- Hands-on experience with neural network analysis, deep learning frameworks (e.g., TensorFlow, PyTorch), and model evaluation
- Proficiency in Python, R, SQL, and data engineering tools such as Spark or Apache Beam and experience in designing, executing, and analyzing A/B tests
- Ability to develop and optimize ETL pipelines for large-scale data processing
- Solid understanding of causal inference, time series forecasting, and statistical modeling
- Hands-on experience with cloud computing platforms (e.g., AWS, GCP, Azure) and big data tools
- Knowledge in natural language processing (NLP), reinforcement learning, and graph analytics is preferable
Tags & focus areas
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