Data Scientist

SUMMARY:
Data Scientist
 
Qualification & Experience
Minimum

• Bachelor’s degree in Data Science, Computer Science, Statistics, Mathematics, or a related field.
• 3–5 years of experience in data science, analytics, or a related field.
• Proven experience with machine learning, predictive modelling, and statistical analysis.
• Strong proficiency in Python, R, SQL, and data visualisation tools (e.g., Power BI, Tableau).
• Experience with cloud platforms (e.g., AWS, Azure, GCP) and big data technologies (e.g., Spark, Hadoop) is advantageous.
• Familiar with version control systems (e.g., Git) and collaborative development practices.

• Master’s degree in Data Science, Computer Science, Statistics, Mathematics, or related field.
• Experience in healthcare, retail, or insurance data ecosystems

• Develop, implement, and maintain predictive and prescriptive models using machine learning algorithms to forecast business outcomes, enabling proactive decision-making and strategic planning.
• Analyse large and complex datasets using statistical techniques to uncover patterns and trends, driving data-informed insights and operational improvements.
• Monitor model performance using validation metrics and retrain models as needed to maintain accuracy, ensuring continued relevance and reliability of outputs.
• Translate business challenges into analytical problems using structured frameworks, enabling the development of targeted and effective data solutions.

• Collaborate with data engineers to build robust data pipelines and ensure data integrity.
• Maintain and optimize data storage solutions for scalability and performance.
• Identify opportunities for automation in reporting and analysis using scripting and APIs, increasing efficiency, and reducing turnaround time.
• Document methodologies, assumptions, and outcomes in a clear and reproducible format to support transparency, governance, and knowledge sharing.

• Translate complex data into actionable insights that support strategic decision-making.
• Identify trends, patterns, and anomalies that inform business strategies and operational improvements.
• Develop and maintain dashboards and reports for various business units.

• Build end-to-end data science solutions, from prototype to production.
• Integrate models into business applications or platforms using APIs or other deployment methods.
• Monitor deployed models for performance drift and retrain as necessary

• Work closely with business stakeholders to understand requirements and define analytical approaches.
• Communicate findings clearly through presentations, visualisations, and storytelling to enhance stakeholder understanding and engagement.
• Provide training and support to non-technical users on data tools and insights to build analytical capacity, empowering teams to leverage data independently.

• Experiment with new techniques to improve model performance and analytical capabilities fostering innovation and continuous improvement.
• Contribute to the development of best practices, standards, and frameworks within the data science team to ensure consistency and quality.

• Ensure compliance with data privacy regulations by applying ethical data managing practices, protecting sensitive information, and maintaining stakeholder trust.
• Implement model governance practices including documentation, versioning, and audit trails.
• Apply bias mitigation techniques in model development to ensure fairness, accuracy, and responsible AI practices

• Deep understanding of statistical methods, probability theory, linear algebra, and calculus to support model development and data interpretation.
• Advanced proficiency in Python, R, SQL, and familiarity with Java or Scala. Ability to write clean, efficient, and reusable code.
• Experience with supervised and unsupervised learning, deep learning frameworks (e.g., TensorFlow, PyTorch), and model evaluation techniques.
• Knowledge of data warehousing, ETL processes, and working with structured and unstructured data.
• Familiar with cloud platforms (AWS, Azure, GCP), containerization (Docker, Kubernetes), and scalable data solutions.
• Skilled in using tools like Power BI, Tableau

• Ability to approach problems logically, identify root causes, and propose data-driven solutions.
• Understands business operations and can align data science initiatives with strategic goals.
• Continuously seeks new methods, tools, and approaches to improve analytical outcomes and business impact.

• Capable of translating complex data findings into clear, compelling narratives for diverse audiences.
• Builds strong relationships with internal and external stakeholders, understands their needs, and delivers relevant insights.
• Confident in presenting technical content to non-technical audiences, including executives and decision-makers.

• Works effectively with product managers, engineers, analysts, and business leaders to co-create solutions.
• Comfortable working in iterative environments, adapting to changing priorities and feedback.

• Demonstrates the ability to navigate ambiguity with confidence and composure.
• Adapts effectively to shifting priorities, evolving goals, and dynamic business contexts.
• Contributes proactively to refining processes, structures, and ways of working to support organisational growth.
• Brings strong problem-solving skills, flexibility, and resilience, coupled with a learning and growth mindset, to thrive in an agile, high-growth environment.

POSITION INFO:
Data Scientist
 
Qualification & Experience
Minimum

• Bachelor’s degree in Data Science, Computer Science, Statistics, Mathematics, or a related field.
• 3–5 years of experience in data science, analytics, or a related field.
• Proven experience with machine learning, predictive modelling, and statistical analysis.
• Strong proficiency in Python, R, SQL, and data visualisation tools (e.g., Power BI, Tableau).
• Experience with cloud platforms (e.g., AWS, Azure, GCP) and big data technologies (e.g., Spark, Hadoop) is advantageous.
• Familiar with version control systems (e.g., Git) and collaborative development practices.

• Master’s degree in Data Science, Computer Science, Statistics, Mathematics, or related field.
• Experience in healthcare, retail, or insurance data ecosystems

• Develop, implement, and maintain predictive and prescriptive models using machine learning algorithms to forecast business outcomes, enabling proactive decision-making and strategic planning.
• Analyse large and complex datasets using statistical techniques to uncover patterns and trends, driving data-informed insights and operational improvements.
• Monitor model performance using validation metrics and retrain models as needed to maintain accuracy, ensuring continued relevance and reliability of outputs.
• Translate business challenges into analytical problems using structured frameworks, enabling the development of targeted and effective data solutions.

• Collaborate with data engineers to build robust data pipelines and ensure data integrity.
• Maintain and optimize data storage solutions for scalability and performance.
• Identify opportunities for automation in reporting and analysis using scripting and APIs, increasing efficiency, and reducing turnaround time.
• Document methodologies, assumptions, and outcomes in a clear and reproducible format to support transparency, governance, and knowledge sharing.

• Translate complex data into actionable insights that support strategic decision-making.
• Identify trends, patterns, and anomalies that inform business strategies and operational improvements.
• Develop and maintain dashboards and reports for various business units.

• Build end-to-end data science solutions, from prototype to production.
• Integrate models into business applications or platforms using APIs or other deployment methods.
• Monitor deployed models for performance drift and retrain as necessary

• Work closely with business stakeholders to understand requirements and define analytical approaches.
• Communicate findings clearly through presentations, visualisations, and storytelling to enhance stakeholder understanding and engagement.
• Provide training and support to non-technical users on data tools and insights to build analytical capacity, empowering teams to leverage data independently.

• Experiment with new techniques to improve model performance and analytical capabilities fostering innovation and continuous improvement.
• Contribute to the development of best practices, standards, and frameworks within the data science team to ensure consistency and quality.

• Ensure compliance with data privacy regulations by applying ethical data managing practices, protecting sensitive information, and maintaining stakeholder trust.
• Implement model governance practices including documentation, versioning, and audit trails.
• Apply bias mitigation techniques in model development to ensure fairness, accuracy, and responsible AI practices

• Deep understanding of statistical methods, probability theory, linear algebra, and calculus to support model development and data interpretation.
• Advanced proficiency in Python, R, SQL, and familiarity with Java or Scala. Ability to write clean, efficient, and reusable code.
• Experience with supervised and unsupervised learning, deep learning frameworks (e.g., TensorFlow, PyTorch), and model evaluation techniques.
• Knowledge of data warehousing, ETL processes, and working with structured and unstructured data.
• Familiar with cloud platforms (AWS, Azure, GCP), containerization (Docker, Kubernetes), and scalable data solutions.
• Skilled in using tools like Power BI, Tableau

• Ability to approach problems logically, identify root causes, and propose data-driven solutions.
• Understands business operations and can align data science initiatives with strategic goals.
• Continuously seeks new methods, tools, and approaches to improve analytical outcomes and business impact.

• Capable of translating complex data findings into clear, compelling narratives for diverse audiences.
• Builds strong relationships with internal and external stakeholders, understands their needs, and delivers relevant insights.
• Confident in presenting technical content to non-technical audiences, including executives and decision-makers.

• Works effectively with product managers, engineers, analysts, and business leaders to co-create solutions.
• Comfortable working in iterative environments, adapting to changing priorities and feedback.

• Demonstrates the ability to navigate ambiguity with confidence and composure.
• Adapts effectively to shifting priorities, evolving goals, and dynamic business contexts.
• Contributes proactively to refining processes, structures, and ways of working to support organisational growth.
• Brings strong problem-solving skills, flexibility, and resilience, coupled with a learning and growth mindset, to thrive in an agile, high-growth environment.