Job Description
Are you ready to shape the future of artificial intelligence? Nexus AI Analytics is seeking a visionary Senior Data Scientist to join our high-impact team in the heart of San Francisco. In this role, you will architect complex machine learning models, derive actionable insights from massive datasets, and partner with stakeholders to solve mission-critical business challenges.
We value intellectual curiosity, rigorous engineering standards, and the ability to turn ambiguity into clear, data-driven strategy. If you are passionate about building scalable, production-grade AI solutions, we want to hear from you.
Responsibilities
- Design, develop, and deploy end-to-end machine learning models to optimize product features.
- Collaborate with cross-functional teams to identify high-impact opportunities for data-driven improvement.
- Perform deep-dive exploratory data analysis to uncover hidden trends and consumer behaviors.
- Maintain production-grade code and develop automated monitoring for model performance and data drift.
- Mentor junior team members and contribute to the growth of our data science engineering culture.
- Communicate complex statistical findings to non-technical stakeholders through compelling data storytelling.
- Stay at the forefront of AI research and integrate cutting-edge methodologies into our internal tech stack.
Qualifications
- Master’s or Ph.D. in Computer Science, Statistics, Mathematics, or a related quantitative field.
- 5+ years of professional experience in data science, machine learning, or quantitative research.
- Expertise in Python or R, with deep proficiency in libraries like PyTorch, TensorFlow, or Scikit-Learn.
- Advanced knowledge of SQL and experience working with large-scale distributed data systems (e.g., Spark, BigQuery).
- Strong understanding of statistical modeling, experimental design, and A/B testing methodologies.
- Proven ability to bridge the gap between technical complexity and business-driven outcomes.
- Experience deploying models into production environments using Docker, Kubernetes, or cloud ML pipelines (AWS/GCP).