Job Description
Are you ready to redefine the future of decision intelligence? Quantum Analytics Group is seeking a visionary Senior Data Scientist to join our elite AI & Machine Learning division in the heart of San Francisco.
In this role, you won't just analyze data—you will build the architectural backbone of our predictive ecosystem. We are looking for a hybrid expert who blends deep mathematical rigor with a product-centric mindset. You will collaborate with cross-functional teams to translate complex business challenges into scalable algorithmic solutions that drive multi-million dollar impacts.
Our culture thrives on technical excellence, curiosity, and rapid iteration. If you are passionate about pushing the boundaries of what is possible with large-scale datasets and state-of-the-art neural networks, we want to meet you.
Responsibilities
- Design, develop, and deploy production-grade machine learning models to optimize customer lifetime value and churn prediction.
- Architect end-to-end data pipelines in collaboration with Data Engineering to ensure high-fidelity inputs for real-time inference.
- Lead experimental design and A/B testing frameworks to validate model performance and business hypothesis.
- Synthesize complex technical findings into actionable executive insights for stakeholders across Product and Finance.
- Mentor junior data scientists and contribute to our internal ML Ops best practices and documentation.
- Stay at the forefront of AI research, implementing SOTA techniques in Natural Language Processing and Computer Vision where applicable.
Qualifications
- Master’s or Ph.D. in Computer Science, Statistics, Mathematics, or a related quantitative field.
- 5+ years of professional experience in data science, with a proven track record of deploying models at scale.
- Expertise in Python and its ecosystem (NumPy, Pandas, Scikit-Learn, PyTorch, or TensorFlow).
- Deep proficiency in SQL and experience working with cloud data warehouses like Snowflake, BigQuery, or Redshift.
- Strong understanding of statistical modeling, Bayesian inference, and causal discovery techniques.
- Excellent communication skills with the ability to articulate technical concepts to non-technical audiences.
- Experience with CI/CD for Machine Learning (MLOps) and containerization tools like Docker/Kubernetes.