Data Scientist

Primary Responsibilities: Project Execution: Gain expertise in specific business areas and contribute to shaping project roadmaps. Tackle ambiguous problems independently, taking full ownership of solutions. Collaborate with cross-functional teams to refine concepts and prototypes, preparing them for stakeholder presentations. Research, build, and implement data models and systems for stakeholders, presenting clear insights and actionable tools. Partner with business leaders to provide data-backed guidance. Provide ongoing maintenance and support for data science solutions and projects. Contribute to decision-making systems, ensuring they are well-supported and effective. Review and help improve technical documentation. Data Support: Convert complex analytical outcomes into actionable business strategies. Develop an in-depth understanding of the organization's data, its underlying patterns, and operational aspects. Extract and analyze data from diverse sources to identify new opportunities or address issues. Actively contribute to the design of innovative data-driven solutions that create value. Collaboration and Mentorship: Contribute to the development of data mining strategies, modeling standards, and analysis methods. Guide junior data scientists by sharing best practices and offering mentorship in machine learning. Accountability and Ownership: Take full responsibility for ensuring the quality of the code. Evaluate and monitor the performance and accuracy of models and systems. Keep track of deployed decision-making tools to ensure their effectiveness. System Architecture: Identify and assess dependencies and impacts across systems. Consistently work on refining and optimizing tools, processes, and techniques. Required Skills & Qualifications: Educational background in fields such as Computer Science, Mathematics, or related disciplines. Strong grasp of machine learning concepts, with practical experience. Proficient in experimental design, A/B testing, and conducting statistical evaluations. Hands-on experience in data preprocessing, feature engineering, and anomaly detection. Knowledge of large-scale language models and AI applications. Advanced programming skills in Python, and familiarity with additional languages like JavaScript, TypeScript, or Golang. Expertise in creating and maintaining ETL pipelines and automated data dashboards. Ability to communicate complex technical concepts to non-technical stakeholders. Desirable Skills: Experience in parallel programming or high-performance computing. Familiarity with cloud platforms like AWS or GCP Advanced knowledge in machine learning libraries like TensorFlow or Pytorch. Expertise in advanced topics like deep learning, natural language processing, or recommendation systems. Why Join Us?: Thrive in a culture that promotes growth, collaboration, and innovation. Gain valuable support for professional development and career advancement. Make a lasting impact by contributing to meaningful work within a high-performing team. Collaborate with diverse teams, gaining exposure to multiple facets of data technology. Be part of challenging projects that drive business success.