Job Description
Are you ready to shape the future of technology?
Nexus Future Systems is seeking a visionary Senior Data Scientist to lead our advanced AI initiatives. We are building the next generation of predictive analytics platforms, and we need a technical expert who can bridge the gap between complex algorithms and real-world business impact.
In this role, you will define the data strategy for high-impact projects, mentor junior talent, and deploy cutting-edge machine learning models that scale globally.
Why Join Us?
- Work on state-of-the-art AI/ML projects with a Fortune 500 client base.
- Competitive compensation package with equity options.
- Flexible remote-first culture with premium San Francisco office amenities.
- Continuous learning budget and conference attendance support.
Key Responsibilities:
- Architect and implement scalable machine learning pipelines using Python and cloud-native technologies.
- Conduct deep-dive data analysis to uncover actionable insights that drive revenue growth.
- Collaborate with cross-functional teams (Engineering, Product, Strategy) to translate business requirements into technical solutions.
- Research and integrate emerging AI technologies, including Large Language Models (LLMs) and computer vision.
- Mentor data science teams, ensuring code quality, model robustness, and best practices.
- Optimize model performance for latency and throughput in production environments.
Qualifications:
- Master’s or PhD in Computer Science, Statistics, Mathematics, or a related quantitative field.
- Minimum of 5+ years of professional experience in data science or machine learning engineering.
- Proficiency in Python (PyTorch, TensorFlow, Scikit-learn) and SQL.
- Strong understanding of MLOps, data warehousing (Snowflake/BigQuery), and cloud platforms (AWS/GCP).
- Proven track record of deploying production-grade models that impact business KPIs.
- Excellent communication skills with the ability to explain complex concepts to non-technical stakeholders.
Responsibilities
- Architect and implement scalable machine learning pipelines using Python and cloud-native technologies.
- Conduct deep-dive data analysis to uncover actionable insights that drive revenue growth.
- Collaborate with cross-functional teams (Engineering, Product, Strategy) to translate business requirements into technical solutions.
- Research and integrate emerging AI technologies, including Large Language Models (LLMs) and computer vision.
- Mentor data science teams, ensuring code quality, model robustness, and best practices.
- Optimize model performance for latency and throughput in production environments.
Qualifications
- Master’s or PhD in Computer Science, Statistics, Mathematics, or a related quantitative field.
- Minimum of 5+ years of professional experience in data science or machine learning engineering.
- Proficiency in Python (PyTorch, TensorFlow, Scikit-learn) and SQL.
- Strong understanding of MLOps, data warehousing (Snowflake/BigQuery), and cloud platforms (AWS/GCP).
- Proven track record of deploying production-grade models that impact business KPIs.
- Excellent communication skills with the ability to explain complex concepts to non-technical stakeholders.