Lead Data Scientist

Role Synopsis

This role is a senior-level position within a data science team, responsible for providing leadership, strategic direction, and technical expertise in the field of data science and machine learning. This role involves leading all aspects of and guiding a team of data scientists while collaborating closely with multi functional departments, such as engineering, product management, and business collaborators. The Lead Data Scientist plays a pivotal role in crafting data-driven strategies and solutions that chip in to the organization's success and growth

Key Accountabilities

• Data Analysis and Modeling:  Lead data scientists must have a confirmed foundation in data analysis and a deep understanding of various machine learning algorithms They should be able to apply these techniques to address sophisticated problems and extract valuable insights from data
• Out of Code computing:  Use libraries that support out-of-core computing, such as Dask in
• Python These libraries can process data that doesn't fit into memory by reading it in smaller portions from disk
• Business Insight:  Understanding the FDO's business objectives and aligning data initiatives with them
• Project Management:  Being skilled in project management methodologies helps in planning and driving data science projects efficiently Machine Learning: Innovation and Strategy - Advanced
• Machine Learning Skills for complex models. Evaluation
• Collaboration and Communication:  Effective communication with collaborators, Explain the modeling approach and results. Implement to privacy guidelines and recommendation with conscious balance
• Continuous Learning:  Staying up-to-date in competitive edge. Apply methodologies to practical business challenges. Meeting with domain GPO's
• Data cleaning and preprocessing, analysis:  The ability to clean and preprocess data effectively is a fundamental skill in any data science role
• Data Ethics and Privacy:  Open communication with customer. Ethical considerations in algorithm design. Secure data handling, Data minimization
• Database Management:  Proficient in database systems and SQL is required for data retrieval and storage
• Domain knowledge:  Expertise in the domain they are working in to understand the context and requirements of the data projects better
• Statistical Analysis and Mathematics:  Solid grasp of statistical methods and mathematical concepts is needed for data analysis, modeling, and drawing substantial insights from data

Experience and Job Requirements

• Data Science Team plays a crucial role in driving data-informed decision-making and generating actionable insights to support the company's goals. This team is responsible for processing, analyzing, and interpreting large and complex datasets from multiple datasets to provide valuable insights and recommendations across various domains. Through advanced analytical techniques and machine learning models, the data science team helps optimize processes, predict trends, and build data-driven strategies. 
• A bachelor's or equivalent experience or master's degree in quantitative, qualitative field such as Computer Science, Statistics, Mathematics, Physics, Engineering, or a related data field is often required
• Skills: Leadership role in Data Analysis, Programming proficiency in Python, SQL, Azure Databricks, Statistics & Mathematics.
• Leadership qualities to steer the team. Strategic direction and technical expertise Soft skills: Active listening, Translate business problems into data questions, Communication and collaboration, Presentation, Problem solving, Multi-functional, Team management, Partner management
• Data Sources: SAP, Concur, Salesforce, Workday, Excel files
• Other: Project management. Domain knowledge [Procurement, Finance, Customer], Business Insight, Critical thinking, Story telling
• Able to prepare analytical reports, presentations and/or visualisation dashboards to communicate findings, important metrics and insights to both technical and non-technical customers
• Stay up to date with industry trends, standard methodologies and new technologies in data analytics, machine learning, data science techniques.