About the Company
At Future Secure AI, we're building something genuinely new — and we're looking for people bold enough to build it with us. We work at the frontier of AI, tackling big, real-world problems for global enterprises across multiple industries, armed with state-of-the-art technology and a culture that prizes courage, rigor, and relentless curiosity. Our BRAVER values aren't just words on a wall — they describe the kind of people we are and the standard we hold ourselves to every day. Our leadership team is entrepreneurial, experienced, and accessible, with an open-door policy that means you'll never be just a number here. We invest seriously in your growth because we know our success depends on yours. If you're ready to work alongside some of the brightest minds in the industry, push into uncharted territory, and do work that genuinely matters, Future Secure AI is the place for you.
About the Role
We are seeking a highly motivated and talented Sr. Data Scientist to join our core Platform Team in Austin, Texas. As an Applied Scientist, you will be a key contributor to the development and improvement of the AI models that power FutureSecure.ai’s proactive intelligence capabilities. You’ll work alongside a team of experienced engineers and scientists, applying machine learning, data mining, and statistical analysis to build, deploy, and optimize our platform for seamless human-AI collaboration. This is a chance to be at the forefront of intelligent interactions and shape the future of work.
Responsibilities
- Model Development & Innovation: Evaluate machine learning models for understanding user intent, predicting workflow needs, and enhancing collaboration between people and AI. This includes exploring new algorithms, techniques, and data sources
- Data Mining & Feature Engineering: Extract, clean, and analyze large volumes of interaction data from various sources to identify patterns and develop impactful features for our AI models. This will focus on understanding how people work alongside AI systems
- Model Deployment & Monitoring: Collaborate with engineering teams to deploy machine learning models into production environments and continuously monitor their performance, identifying areas for improvement and retraining
- Research & Exploration: Stay abreast of the latest advancements in machine learning, data science, and human-computer interaction, and apply them to enhance our platform’s capabilities
- Experimentation & A/B Testing: Design and conduct experiments to evaluate the effectiveness of new models and features, using rigorous A/B testing methodologies to optimize for user experience and collaboration