Job Description
We are seeking a visionary Senior Data Scientist to lead our advanced analytics initiatives in San Francisco. At Nexus Analytics, we believe that data is the most valuable asset for driving business growth. You will work at the intersection of Artificial Intelligence and Business Intelligence, transforming complex datasets into actionable strategies that impact millions.
In this role, you will mentor junior data scientists, architect robust machine learning models, and collaborate closely with cross-functional product teams to solve intricate business problems. If you are passionate about building scalable systems and have a knack for extracting insights from big data, we want to hear from you.
Why Join Us?
- Competitive salary and equity package.
- Top-tier health and wellness benefits.
- Flexible remote/hybrid work options.
- Continuous learning and development budget.
Responsibilities
- Model Development: Design, train, and deploy state-of-the-art machine learning models and statistical algorithms to solve real-world business challenges.
- Data Architecture: Build scalable data pipelines and ETL processes to ensure high-quality, clean, and reliable data for analysis.
- Stakeholder Communication: Translate complex technical findings into clear, compelling narratives for executive leadership and non-technical teams.
- Experimentation: Conduct A/B testing and statistical experiments to validate hypotheses and optimize product features.
- Team Leadership: Mentor junior data scientists and data engineers, fostering a culture of innovation and best practices.
- Technology Stack: Stay current with emerging technologies in the AI space and recommend tools to improve our analytical capabilities.
Qualifications
- Education: Master’s or Ph.D. degree in Computer Science, Statistics, Mathematics, or a related quantitative field.
- Experience: 5+ years of professional experience in data science, machine learning, or analytics roles.
- Programming: Expert proficiency in Python (Pandas, NumPy, Scikit-learn) and SQL. Experience with R or Scala is a plus.
- Machine Learning: Strong foundation in classical ML techniques (Regression, Clustering) and deep learning frameworks (TensorFlow, PyTorch).
- Cloud Experience: Proven track record of deploying models in cloud environments (AWS, GCP, or Azure).
- Problem Solving: Demonstrated ability to tackle ambiguous problems and derive insights from unstructured data.