Job Purpose:

The Data Scientist will develop and implement data science models and analytical solutions that address business challenges and uncover growth opportunities.

The Data Scientist applies statistical techniques, machine learning algorithms, and data science methods to derive insights and support evidence-based decisions.

Key Responsibilities:

Strategic:

• Stay abreast of new ML/AI methods and propose applicable solutions.
• Align model development efforts with broader team priorities and ethics guidelines.
• Provide input into project scoping and business value estimation.

Initiatives:

• Design, develop, and deploy machine learning models.
• Clean, prepare, and engineer features from structured and unstructured data.
• Collaborate with stakeholders to ensure models address real business problems.
• Present insights and model results in understandable formats

Operational:

• Write production-ready code and maintain model pipelines.
• Conduct peer reviews and contribute to code repositories.
• Document assumptions, methodologies, and model limitations.
• Monitor model performance and recalibrate as needed.
• Contribute to internal knowledge sharing and continuous learning efforts.

Key Duties:

Problem Scoping:

• Work with business teams to define problems.
• Frame use cases into model-ready formats.
• Perform initial feasibility assessments.

Data Preparation:

• Extract, clean, and prepare data.
• Conduct exploratory data analysis.
• Engineer relevant features.

Model Development:

• Build machine learning/statistical models.
• Train and tune models using appropriate metrics.
• Evaluate performance and robustness.

Insight Communication:

• Visualize and interpret results.
• Translate findings into business language.
• Support adoption and stakeholder understanding.

Deployment and Monitoring:

• Package models for deployment (API, batch, etc.).
• Monitor performance and drift.
• Maintain logs and feedback loops.

Collaboration and Learning:

• Participate in peer reviews and team learning.
• Stay updated on new tools/techniques.
• Contribute to knowledge sharing.

Academic Qualifications:

Bachelor’s degree in computer science, Statistics, Mathematics, Engineering, or a related quantitative field.

Professional Qualifications / Membership to professional bodies/ Publication:

• Practical ML certifications (e.g. Coursera, Udacity, DataCamp).
• Python, R, or SQL proficiency certifications.
• Cloud ML certifications (AWS, Azure, GCP) are a plus.

Work Experience Required:

2–5 years in data science, machine learning, or predictive modeling roles

Key Competencies:

• Statistical & Machine Learning Knowledge: applies predictive modeling, classification, clustering, and other techniques to solve real problems.
• Programming Proficiency: proficient in Python, R, and SQL, with hands-on experience using data science libraries and frameworks.
• Data Wrangling & Feature Engineering: transforms raw data into meaningful inputs for models through cleaning, joining, and deriving features.
• Curiosity & Innovation: constantly seeks new methods, algorithms, and technologies to improve performance.
• Model Deployment & Monitoring: familiar with putting models into production, versioning, and tracking performance over time.
• Communication of Insights: translates complex analysis into clear, actionable insights tailored to the business audience.
• Collaboration & Teamwork: works effectively with analysts, engineers, and business teams to drive outcomes.
• Adaptability: quickly learns new tools and adjusts to changing priorities or technologies.

• Stay abreast of new ML/AI methods and propose applicable solutions.
• Align model development efforts with broader team priorities and ethics guidelines.
• Provide input into project scoping and business value estimation.
• Design, develop, and deploy machine learning models.
• Clean, prepare, and engineer features from structured and unstructured data.
• Collaborate with stakeholders to ensure models address real business problems.
• Present insights and model results in understandable formats
• Write production-ready code and maintain model pipelines.
• Conduct peer reviews and contribute to code repositories.
• Document assumptions, methodologies, and model limitations.
• Monitor model performance and recalibrate as needed.
• Contribute to internal knowledge sharing and continuous learning efforts.
• Work with business teams to define problems.
• Frame use cases into model-ready formats.
• Perform initial feasibility assessments.
• Extract, clean, and prepare data.
• Conduct exploratory data analysis.
• Engineer relevant features.
• Build machine learning/statistical models.
• Train and tune models using appropriate metrics.
• Evaluate performance and robustness.
• Visualize and interpret results.
• Translate findings into business language.
• Support adoption and stakeholder understanding.
• Package models for deployment (API, batch, etc.).
• Monitor performance and drift.
• Maintain logs and feedback loops.
• Participate in peer reviews and team learning.
• Stay updated on new tools/techniques.
• Contribute to knowledge sharing.

• Statistical & Machine Learning Knowledge
• Programming Proficiency (Python, R, SQL)
• Data Wrangling & Feature Engineering
• Curiosity & Innovation
• Model Deployment & Monitoring
• Communication of Insights
• Collaboration & Teamwork
• Adaptability

• Practical ML certifications (e.g. Coursera, Udacity, DataCamp).
• Python, R, or SQL proficiency certifications.
• Cloud ML certifications (AWS, Azure, GCP) are a plus.