Job Description
NexaCore AI Solutions is a leading innovator in predictive analytics and machine learning. We are seeking a visionary Senior Data Scientist to join our elite Engineering and Analytics team in San Francisco. In this role, you will bridge the gap between complex data and strategic decision-making, leveraging massive datasets to build models that redefine our industry landscape. You will work in a high-velocity environment, collaborating with cross-functional teams to deploy scalable AI solutions that impact millions of users.
The ideal candidate is not just a technician but a storyteller who can translate numbers into actionable business narratives. At NexaCore, we value intellectual curiosity, technical rigor, and a commitment to building ethical, high-performance algorithms.
Responsibilities
- Design, develop, and deploy production-grade machine learning models to solve complex business problems.
- Lead the end-to-end data science lifecycle, from data collection and feature engineering to model validation and monitoring.
- Architect scalable data pipelines in collaboration with Data Engineering to ensure high-quality data availability.
- Conduct advanced statistical analysis and A/B testing to optimize product features and user engagement.
- Communicate technical findings and strategic recommendations to executive leadership and non-technical stakeholders.
- Mentor junior data scientists and contribute to a culture of continuous learning and technical excellence.
- Stay at the forefront of AI research, implementing cutting-edge techniques in NLP, Computer Vision, or Reinforcement Learning where applicable.
Qualifications
- Master’s or PhD in Computer Science, Statistics, Mathematics, or a related quantitative field.
- 5+ years of professional experience as a Data Scientist, preferably within a high-growth tech environment.
- Expert-level proficiency in Python and deep familiarity with ML libraries such as PyTorch, TensorFlow, or Scikit-Learn.
- Extensive experience with SQL and handling large-scale datasets using Spark or Snowflake.
- Proven track record of deploying models into production environments using AWS, GCP, or Azure.
- Strong understanding of statistical modeling, experimental design, and optimization algorithms.
- Exceptional communication skills with the ability to influence product roadmap through data-driven insights.
- Experience with containerization (Docker, Kubernetes) and CI/CD for ML (MLOps) is highly desirable.