Data Scientist

Role purpose

• Support the development and deployment of AI, ML, and predictive analytics models in collaboration with senior data scientists
• Work as part of a Data Science team to integrate diverse data sources and enable data-driven decision-making across functional boundaries
• Contribute to innovative analytical solutions that help enable users and decision makers solve business problems and drive measurable outcomes

Accountabilities

• Assist in building predictive models and analytics capabilities under guidance of senior team members
• Perform data analysis and exploratory data analysis (EDA) to develop fact-based insights and recommendations
• Support the integration of traditional (internal) and nontraditional (external) data sources into analytical workflows
• Contribute to data mining initiatives to extract insights from structured and unstructured data
• Conduct data cleaning, preprocessing, and validation to ensure data completeness and quality
• Work with unstructured datasets to identify patterns and emerging trends
• Document data sources, methodologies, and analytical processes
• Maintain data pipelines and support data integration activities
• Work with cross-functional teams (key users, engineers, domain experts) to understand problem statements and requirements
• Translate analytical findings into clear, actionable insights for non-technical audiences
• Participate in agile team ceremonies and contribute to sprint planning and execution
• Engage with business users to understand their needs and validate analytical approaches
• Participate in team knowledge-sharing sessions and training programs
• Learn industry best practices for statistical programming, modeling, and data visualization
• Build domain knowledge in relevant business areas (Marketing, Sales, Supply Chain, R&D)

Critical success factors & key challenges

• Clear Communication:

  Effectively convey analytical scope, limitations, and risks to business

• Data Completeness:

  Ensure all essential information is captured and validated for analysis

• Business Applicability:

  Deliver insights and models that directly address business goals and requirements

• User Engagement:

  Build relationships with business users and demonstrate value through data-driven solutions

• Curate Documentation:

  Keep and archive documentation following the establish good practices

Knowledge, experience, education & capabilities

Critical knowledge:

• Understanding of fundamental data science algorithms including data cleaning, clustering, and pattern recognition
• Basic statistical analysis skills with ability to apply appropriate techniques to business problems
• Ability to translate business questions into analytical approaches with guidance
• Foundational programming skills in Python for data analysis and statistical modeling
• Understanding of data structures, algorithms, and basic software development principles
• Familiarity with data visualization techniques to communicate insights effectively

Critical experience:

• Demonstrated experience with data analysis through internships, academic projects, capstone projects, or entry-level roles
• Hands-on experience with Python and Python Libraries (pandas, numpy, matplotlib/seaborn)
• Exposure to SQL and relational databases for data extraction and manipulation
• Basic understanding of statistical methods and machine learning concepts
• Experience working in team environments (academic group projects, internships, or collaborative work)

Bachelors degree in Computer Science, Mathematics, Statistics, Data Science or related quantitative fields