Data Scientist

We need a Data Scientist with experience to lead the creation of cutting-edge data-driven solutions at our company. The best person for the job knows mathematical modeling, statistics, Machine Learning (ML), and Artificial Intelligence (AI). They are also used to working with current frameworks and large language models (LLMs). You will use your strong problem-solving, critical thinking, and research skills to plan and carry out complete data science projects, try out new methods, share your results, and maybe even come up with intellectual property that can be patented.

Some of your main duties Research and new ideas: Learn more about modern LLM orchestration frameworks and APIs (for example LangChain or equivalent), and work with proprietary and open source LLMs. Encourage new ideas by testing, proving, and studying the viability of new data-driven goods or features. Keep up with new technologies and trends in your field to make sure the company stays on the cutting edge of AI. End-to-End Project Execution: Take charge of data science projects from the time they are first thought of to the time they are put into action. This includes gathering requirements, modeling, and keeping an eye on performance. Partner cross-functionally with Engineering, Product, and Business teams to define backlogs, prioritize work, and deliver data solutions incrementally. Ensure all models and data pipelines adhere to strong quality control, governance, and continuous improvement standards within an Agile framework. Model Development and Deployment: Use both traditional machine learning (ML) and cutting-edge artificial intelligence (AI) methods to design, build, and improve advanced predictive and prescriptive models. To make the model work better, use best practices for feature building and data transformation. Use the best frameworks and methods (like prompt engineering and LLM fine-tuning) to solve hard data problems. Mentorship and technical leadership: Lead by example and push for data-driven decisions across the whole company. Be a mentor to peer data scientists and AI engineers, helping them learn new skills and advance in their careers. Push for best practices in code quality, version control, and being able to make changes again and again. Design and Validation of Experiments: Do thorough tests (A/B testing, hypothesis testing) to confirm how well the model works and how it affects the business. Advanced statistical methods and data visualizations can help you share results with people who don't have the same level of professional knowledge. Knowledge Sharing and Intellectual Property: Put together and talk about study results at company seminars, outside conferences, or events for the industry. Write for academic and business journals and look for ways to protect new ideas. Write down findings, methods, and best practices to help the company learn more. Continual Improvement: Find ways to make data pipelines better, such as how they receive, handle, store, and analyse data, and then put those ideas into action. Use DevOps and MLOps to make the model creation lifecycle easier and make sure that deployments happen all the time.

Master's or Ph.D. in computer science, statistics, math, data science, or a related field, or at least two years of work in a similar role.