Data Scientist

JOB DESCRIPTION:

The Data Scientist plays a key role in the organization by combining data analytics and AI/ML model development to support business strategies and data-driven decision-making. This position spends approximately 20-30% of the time on Data Analytics, using Power BI to build dashboards that visualize results from AI/ML models and guiding end-users from other Departments to build their own dashboards, and approximately 70-80% of the time on Data Science, building and maintaining AI/ML models such as Forecasting Models and Recommender Systems, and collaborating on and managing AI/ML projects to meet business requirements and timelines.

1. Data Analytics & Dashboarding (20-30% of time)

• Build and maintain Power BI dashboards to visualize results and insights from AI/ML models for business stakeholders (Power BI is a must-have skill).
• Guide and support end-users from other Departments in designing and building their own Power BI dashboards.
• Ensure dashboards are accurate, user-friendly, and aligned with business reporting needs.

2. AI/ML Model Development (70-80% of time)

• Build and maintain AI/ML models, such as Forecasting Models and Recommender Systems, to support business applications.
• Collect, process, clean, and standardize data from various sources for use in AI/ML models and dashboards.
• Research and apply appropriate algorithms and techniques to solve business forecasting and recommendation problems.

3. Model Evaluation & Optimization

• Evaluate model performance using appropriate metrics and validation techniques, and fine-tune models for specific use cases.
• Optimize models for accuracy, stability, and scalability.
• Conduct A/B testing and model comparisons to select the best solution.

4. AI/ML Project Management

• Collaborate on and manage AI/ML projects to meet business requirements and project timelines.
• Coordinate with stakeholders to define project scope, priorities, and deliverables.
• Track project progress and proactively flag risks, delays, or resource needs.

5. Collaboration with Cross-functional Teams

• Collaborate with cross-functional teams (Business, IT, Operations, etc.) to identify needs and translate them into analytical and AI/ML solutions.
• Communicate technical concepts, model results, and dashboard insights to non-technical stakeholders.
• Provide guidance and support to end-users on data analytics tools and dashboard best practices. Collaborate with Finance, Sales, Marketing, SCM, Purchasing, Operations, Technical, Internal Control, IT and other teams to ensure data-driven decision-making.

6. Documentation & Reporting

• Document model development processes, dashboard designs, and analytical results.
• Prepare regular reports on project outcomes, model performance, and business impact.
• Maintain clear and comprehensive technical documentation for future reference.

7. Research & Innovation

• Stay updated with the latest advancements in Data Science, AI/ML, and data visualization tools such as Power BI.
• Propose and implement innovative solutions to improve business processes.
• Participate in knowledge sharing and training sessions within the organization.

8. Ethical AI & Compliance

• Ensure AI/ML models and dashboards comply with relevant data privacy, security, and ethical standards.
• Assess and mitigate risks related to bias, fairness, and transparency in AI models.

9. Continuous Improvement

• Continuously monitor and improve existing AI/ML models and dashboards.
• Gather feedback from users and stakeholders to enhance solution effectiveness.
• Identify opportunities for automation and process optimization using AI and data analytics.

JOB REQUIREMENT:

1. Education: Bachelor's degree in Data Science, Statistics, Computer Science, Applied Mathematics, or related fields. Solid foundation in Machine Learning and Data Visualization is preferred.

2. Experience:

At least 1-3 years of experience in Data Science / Data Analytics roles, building AI/ML models (e.g., Forecasting Models, Recommender Systems) and Power BI dashboards.

3. Communication & Presentation skills:

Excellent communication skills to present technical and analytical results to non-technical stakeholders, and to guide end-users in building dashboards.

4. Programming & Technical Skills:

Proficient in Power BI (must-have) for dashboard building. Strong skills in Python or R, SQL, and machine learning libraries (e.g., Scikit-learn, TensorFlow). Experience with cloud platforms (Azure, AWS, GCP) is a plus.

5. Other Accountability:

Strong problem-solving and project management mindset, proactive in learning new technologies and keeping up with AI and data analytics trends.

6. Language skills:

Good English skills (reading technical documents, communicating with international teams).