Data Science Manager

As a Data Science Manager in the Research and Development (R&D) team at our organization, you will play a crucial role in driving innovation through advanced machine learning and AI algorithms. Your primary responsibility will involve conducting applied research, development, and validation of cutting-edge algorithms to address complex real-world problems on a large scale. - Collaborate closely with the product team to gain insights into business challenges and product objectives - Devise creative algorithmic solutions - Create prototypes and demonstrations to validate new ideas - Transform research findings into practical innovations by collaborating with AI Engineers and software engineers - Formulate and execute research plans - Carry out experiments, document and consolidate results - Potentially publish your work - Safeguard intellectual property resulting from R&D endeavors - Mentor junior staff - Ensure adherence to established procedures - Collaborate with various stakeholders, academic/research partners, and fellow researchers to deliver tangible outcomes To excel in this position, you are required to possess: - A strong foundation in computer science principles - Proficient skills in analyzing and designing AI/Machine learning algorithms - Practical experience in key areas such as supervised and unsupervised machine learning, reinforcement learning, deep learning, knowledge-based systems, evolutionary computing, probabilistic graphical models, among others - Adept in at least one programming language - Hands-on experience in implementing AI/machine learning algorithms using Python or R - Familiarity with tools, frameworks, and libraries like Jupyter/Zeppelin, scikit-learn, matplotlib, pandas, Tensorflow, Keras, Apache Spark, etc. Ideally, you should have at least 2-5 years of applied research experience in solving real-world problems using AI/Machine Learning techniques. Additionally, having a publication in a reputable conference or journal related to AI/Machine Learning or holding patents in the field would be beneficial. Experience in contributing to open-source projects within the AI/Machine Learning domain will be considered a strong asset.,