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.