Principal data scientist

The Principal Data Scientist develops and implements algorithms, data analysis, and insights to drive business innovation and efficiency. This role involves leading data science projects, collaborating with cross‑functional teams, and ensuring the quality and impact of data science solutions.Responsibilities and DutiesLead the development and implementation of data science projects, including the design and development of algorithms and models.Collaborate with stakeholders to understand business requirements and translate them into data science solutions.Analyze large datasets to extract insights, identify trends, and support decision‑making.Develop and validate predictive models, machine learning algorithms, and statistical analyses.Ensure the accuracy, quality, and relevance of data science outputs.Stay updated with the latest advancements in data science and machine learning, applying them to enhance solutions.Mentor and provide guidance to junior data scientists and other team members.Ensure compliance with data governance, security, and regulatory standards in all data science activities.Prepare and present data science reports and documentation to senior management and stakeholders.Participate in project planning and contribute to the development of project timelines and deliverables.Perform other duties relevant to the job as assigned by the Head of Data & AI Engineering or senior management.RequirementsBachelor’s degree in Data Science, Computer Science, Statistics, or a related fieldRelevant certifications (e.g., Certified Data Scientist, Google Cloud Professional Data Engineer) are preferredMinimum of 8 years of experience in data science or related fieldsExperience in developing and implementing data science solutions for AI or technology‑focused productsStrong programming skills in languages such as Python, SQLProficiency in data science tools and frameworks (e.g., Tensor Flow, Py Torch, Scikit‑learn)Excellent problem‑solving and analytical skillsStrong communication and interpersonal skillsAttention to detail and commitment to qualityIn‑depth understanding of data science principles, machine learning algorithms, and statistical analysisFamiliarity with data visualization tools (e.g., Tableau, Power BI)Knowledge of data governance, security, and regulatory standardsAbility to manage multiple tasks and prioritize effectivelyStrong attention to detail and commitment to delivering high‑quality workAbility to work independently and as part of a teamProgramming languages (e.g., Python, SQL)Data science tools and frameworks (e.g., Tensor Flow, Py Torch, Scikit‑learn)Data visualization tools (e.g., Tableau, Power BI)Collaboration and communication tools (e.g., Slack, Microsoft Teams)Data management systems (e.g., SQL, No SQL databases)