Data Scientist

malomatia; Doha; On-site; QAR 120,000 - 180,000; Full time

Job summary

malomatia in Doha is looking for a Data Scientist with 3-5 years of experience in data science or analytics. The successful candidate will have proficiency in Python, SQL, and core machine learning techniques.

The role involves building and evaluating predictive models, conducting exploratory data analysis, and creating visualizations to communicate findings effectively to stakeholders.

Qualifications

• 3–5 years of experience in data science, analytics, or a quantitative modeling role.
• Hands‑on proficiency in Python and SQL for building and evaluating models.
• Bachelor's degree in a quantitative field or equivalent experience.

Responsibilities

• Conduct exploratory data analysis to uncover patterns in client data.
• Build, train, and evaluate predictive models using Python and SQL.
• Create clear visualizations and dashboards to translate analysis into business insight.

Skills

Data science

Python

SQL

Machine learning

Data analysis

Education

Bachelor's degree in a quantitative field

Tools

TensorFlow

PyTorch

Power BI

Tableau

Job description

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.
• Ability to communicate findings clearly to both technical colleagues and business stakeholders.

Nice to Have

• 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.

Responsibilities

• 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.
• Maintain and improve existing analytical code and notebooks.

Qualifications

• 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.