Description:
Neural Surge Ai is looking for a Senior Data Scientist who thrives at the intersection of product strategy and advanced engineering. You will be responsible for the entire data lifecycle. From translating ambiguous business problems into rigorous technical documentation to designing the product logic and finally leading the hands-on execution of scalable AI/ML solutions.
1. Technical Documentation & Strategy
- Translate Business Needs: Convert high-level executive goals into detailed technical requirement documents (TRDs).
- Architectural Mapping: Define the data structures, model requirements, and API integrations necessary to support new business verticals.
- Feasibility Analysis: Conduct rapid prototyping and data exploration to validate product concepts before full-scale development begins.
2. Product Design & Logic
- Algorithmic Design: Design the "brain" of our internal applications, ensuring that data models align with user experience and business logic.
- System Synthesis: Work closely with engineering teams to ensure data models are integrated seamlessly into unified application frameworks.
- Metric Frameworks: Define what success looks like by designing KPIs and automated dashboards to track model performance and product impact.
3. Execution & Deployment
- End-to-End Development: Clean, analyze, and build production-ready models using Python (Scikit-learn, PyTorch, or TensorFlow).
- DevOps Integration: Collaborate on version control (Git) and deployment workflows to move models from local to enterprise-grade environments.
- Technical Leadership: Lead a talented team of engineers and data scientists, providing code reviews, mentorship, and architectural guidance to ensure high-quality delivery.
Required Qualifications
- Experience: 5+ years in Data Science with a proven track record of shipping data-driven products (not just research).
- Tech Stack: Mastery of Python and SQL; experience with Git workflows and deployment frameworks.
- Communication: Ability to explain complex "black box" models to non-technical stakeholders and translate their feedback into technical tasks.
- Leadership: Experience managing or mentoring technical teams in a fast-paced, agile environment.