Job Description
Are you ready to transform massive datasets into actionable strategic intelligence? Nexus AI Analytics is looking for a Senior Data Scientist to join our elite core team in San Francisco. You will be at the forefront of building scalable machine learning models that drive product innovation for our global client base.
We offer a collaborative environment where cutting-edge research meets real-world application. If you are passionate about predictive modeling, neural networks, and optimizing complex systems, this is your opportunity to define the future of data-driven decision-making.
Responsibilities
- Design, develop, and deploy end-to-end machine learning pipelines to solve complex business problems.
- Collaborate with cross-functional product and engineering teams to integrate predictive models into production environments.
- Perform deep-dive exploratory data analysis to identify trends, patterns, and optimization opportunities.
- Develop robust A/B testing frameworks to measure the impact of model deployments.
- Mentor junior data scientists and contribute to our internal knowledge-sharing initiatives.
- Translate technical findings into executive-level dashboards and actionable strategic recommendations.
- Continuously evaluate emerging open-source technologies to enhance our existing data infrastructure.
Qualifications
- Master’s or PhD degree in Computer Science, Statistics, Mathematics, or a related quantitative field.
- 5+ years of professional experience in data science, machine learning, or quantitative research.
- Expert-level proficiency in Python or R, with deep familiarity with scikit-learn, TensorFlow, or PyTorch.
- Strong background in statistical modeling, hypothesis testing, and experimental design.
- Demonstrated expertise in SQL and working with cloud-based data warehouses like Snowflake, BigQuery, or AWS Redshift.
- Proven ability to communicate complex data concepts to non-technical stakeholders effectively.
- Experience with distributed computing frameworks such as Apache Spark or Dask is highly preferred.