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Data Scientist
Job Description
Must Have
Must Have
- 3–5 years of experience in data science, analytics, or a quantitative modeling role.
- Hands-on proficiency in Python (pandas, NumPy, 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.
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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.
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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 TensorFlow 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.
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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, NumPy, 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 TensorFlow 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.
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