Job Description
About Nexus AI Systems
We are a leading frontier technology firm driving innovation in predictive analytics and artificial intelligence. Our mission is to democratize data-driven decision-making for enterprise clients worldwide. We are seeking a visionary Senior Data Scientist to join our elite research team and architect the next generation of machine learning models.
Why Join Us?
- Work on cutting-edge AI infrastructure that impacts millions of users.
- Competitive compensation package and equity opportunities.
- Flexible remote-first culture with a presence in the heart of SF.
The Role
In this high-impact position, you will own the end-to-end lifecycle of data science projectsāfrom raw data ingestion to model deployment in production environments. You will work closely with product managers, engineers, and stakeholders to translate complex business challenges into robust algorithmic solutions.
Responsibilities
- Model Development: Design, train, and optimize complex machine learning models (e.g., NLP, Computer Vision, Recommender Systems) to solve high-impact business problems.
- Production Deployment: Deploy scalable models to cloud infrastructure (AWS/Azure) using containerization tools like Docker and Kubernetes.
- Data Strategy: Lead the data strategy, including feature engineering, data pipeline architecture, and ensuring data quality and integrity.
- Experimentation: Design and execute A/B testing frameworks to validate model performance and drive product iteration.
- Stakeholder Communication: Translate technical findings into actionable business insights for non-technical stakeholders and executive leadership.
Qualifications
- Education: Masterās or PhD in Computer Science, Statistics, Mathematics, or a related quantitative field.
- Experience: 5+ years of professional experience in data science, machine learning engineering, or a similar analytical role.
- Programming: Expert proficiency in Python (Pandas, NumPy, Scikit-learn) and SQL.
- Frameworks: Deep experience with Deep Learning frameworks such as TensorFlow, PyTorch, or Keras.
- Cloud Computing: Proven track record of deploying models in cloud environments (AWS, GCP, or Azure).
- Problem Solving: Strong ability to deconstruct ambiguous problems and derive data-driven hypotheses.