Job Description
Are you ready to transform massive datasets into strategic business advantages? Nexus AI Analytics is seeking a visionary Senior Data Scientist to join our elite engineering team in San Francisco. You will leverage cutting-edge machine learning and predictive modeling to solve complex challenges for global enterprise clients.
We are a high-growth firm where data-driven decisions sit at the heart of everything we do. If you thrive in a fast-paced environment and have a passion for operationalizing AI at scale, we want to hear from you.
Responsibilities
- Design, build, and deploy scalable machine learning models to solve high-impact business problems.
- Collaborate with cross-functional product and engineering teams to integrate AI solutions into production platforms.
- Perform deep-dive exploratory data analysis to uncover hidden trends and actionable insights.
- Develop robust data pipelines and feature engineering workflows to improve model performance.
- Mentor junior data scientists and contribute to the technical excellence of the AI division.
- Communicate complex analytical findings to non-technical stakeholders through compelling data storytelling.
- Stay at the forefront of AI research and explore new algorithms to maintain our competitive edge.
Qualifications
- Master's or PhD in Computer Science, Statistics, Mathematics, or a related quantitative field.
- 5+ years of professional experience in data science, with a focus on predictive modeling and machine learning.
- Expert-level proficiency in Python and deep knowledge of ML frameworks such as PyTorch, TensorFlow, or Scikit-Learn.
- Hands-on experience with cloud platforms (AWS, GCP, or Azure) and containerization tools like Docker/Kubernetes.
- Deep understanding of SQL and experience working with large-scale distributed data systems (Spark, Snowflake, BigQuery).
- Strong analytical mindset with the ability to bridge the gap between technical complexity and business requirements.
- Excellent communication skills with experience presenting findings to executive leadership.