Data Scientist

Must Have

• 3–5 years of experience in data science, analytics, or a quantitative modeling role.
• Hands‑on proficiency in Python (pandas, Num Py, scikit‑learn) and SQL for building and evaluating models end to end.
• Demonstrated ability to design experiments, validate model performance, and guard against overfitting and data leakage.
• Clear, reproducible, and well‑documented analytical practice.
• Ability to communicate findings clearly to both technical colleagues and business stakeholders.

• Experience with visualization tools such as matplotlib, seaborn, Power BI, or Tableau.
• Exposure to cloud analytics environments, ideally Oracle Cloud Infrastructure (OCI).
• Experience with time‑series analysis or natural‑language data.
• Familiarity with collaborative workflows and code review.
• Exposure to working with engineering teams on model handoff.
• Data science certifications.

• Conduct exploratory data analysis to uncover patterns, relationships, and opportunities in client and operational data.
• Build, train, and evaluate predictive and descriptive models using Python (pandas, scikit‑learn) and SQL.
• Build and evaluate deep learning models, such as neural networks, using frameworks like Tensor Flow or PyTorch where they suit the problem.
• Perform feature engineering, data cleaning, and dataset preparation for modeling.
• Design and analyze experiments, including A/B tests, to measure the impact of changes and interventions.
• Prototype analytical solutions and iterate on them based on stakeholder and senior data scientist feedback.
• Create clear visualizations, dashboards, and summaries that translate analysis into business insight.
• Validate model performance using appropriate metrics and guard against overfitting and data leakage.
• Document methodology, assumptions, and results to ensure reproducibility and knowledge sharing.
• Collaborate with senior data scientists, engineers, and analysts on larger initiatives.
• Support the preparation of analytical reports and presentations for clients and internal teams.
• Maintain and improve existing analytical code and notebooks.

• Bachelor's degree in a quantitative field (Statistics, Mathematics, Computer Science, Engineering, Data Science) or equivalent experience;
  Master's an advantage.
• Proficiency in Python for data analysis (pandas, Num Py, scikit‑learn) and working knowledge of SQL.
• Solid grounding in statistics and core machine‑learning techniques (regression, classification, clustering).
• Working knowledge of deep learning concepts and frameworks such as Tensor Flow or PyTorch.
• Experience with feature engineering and preparing real‑world, messy datasets for modeling.
• Ability to communicate findings clearly to technical and business audiences.
• Understanding of model evaluation metrics and validation techniques.
• Familiarity with version control (Git) and reproducible analysis practices.