Data Scientist

Role Overview: As a Data Scientist, you will lead the development of advanced analytics and Generative AI (GenAI) solutions that deliver measurable impact for clients. You will combine deep technical knowledge in machine learning and GenAI with solid software engineering practices to build scalable, robust tools and applications. Responsibilities: Design, build, and deploy advanced analytics and GenAI solutions, including custom agents and LLM-integrated applications. Lead the development of modular, maintainable, and production-ready codebases using best practices in software engineering (e.g., version control, testing, CI/CD). Build interactive apps and dashboards using frameworks like Streamlit, enabling seamless business interaction with AI tools. Develop APIs and microservices to integrate GenAI tools with client systems or platforms. Identify and mitigate risks associated with GenAI (e.g., hallucination, bias, privacy, regulatory concerns), and implement robust logging, monitoring, and evaluation mechanisms. Translate complex analytical and AI concepts into clear recommendations for business and technical stakeholders. Coach and mentor junior data scientists, ensuring high-quality code and technical growth across the team. Collaborate with data engineers, DevOps, and product managers to ensure end-to-end solution delivery. Qualifications: 5+ years of experience in data science, with strong experience delivering ML or GenAI models and applications in production. Proficiency in Python with strong command of relevant libraries (e.g., pandas, scikit-learn, PyTorch, LangChain, FastAPI). Solid software engineering skills: experience with Git, testing frameworks (e.g., pytest), CI/CD pipelines (e.g., GitHub Actions, GitLab CI), and packaging/distribution of code. Experience building modular architectures for GenAI applications and agents (e.g., using LangChain, OpenAI function calling, vector stores).