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.
Roles & Responsibilities
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 Responsibilities:
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.