Senior Data Scientist
Summary
Lead advanced analytics and statistical modeling projects for a government entity in Qatar, translating policy questions into rigorous insights using Python, SQL, and ML techniques while mentoring junior data scientists.
Job De ion Must Have
- 6-10 years of experience in data science, analytics, or applied statistics, including a demonstrable track record of leading projects end to end.
- Senior technical voice on a data science team - setting modeling standards, reviewing peer work, and mentoring less-experienced data scientists.
- Deep command of statistics, experimental design, and a broad modeling toolkit spanning classical machine learning, time-series, and deep learning.
- Demonstrated ability to translate ambiguous business and policy questions into rigorous, decision-ready analysis for executive audiences.
- Commitment to statistical soundness, reproducibility, and continuous learning in statistical and machine-learning methods.
- Experience in the government or large-enterprise sector, ideally in Qatar or the wider GCC.
- Familiarity with Oracle Cloud Infrastructure (OCI) and cloud-based analytics environments.
- Exposure to deploying models into production in partnership with engineering teams.
- Domain expertise in a relevant vertical such as public sector, finance, telecom, or healthcare.
- Experience with causal inference or advanced experimentation methods.
- Working knowledge of data visualization or business-intelligence tools for stakeholder communication.
- Relevant data science or cloud certifications.
- Lead the design and execution of advanced analytics and statistical modeling projects, from problem framing through to validated, decision-ready insight.
- Translate ambiguous business and policy questions into well-defined data science problems, measurable hypotheses, and analytical plans.
- Define and enforce modeling methodology, experimentation standards (including A/B testing and quasi-experimental designs), and model validation practices across the team.
- Build, evaluate, and interpret advanced predictive and statistical models using Python (pandas, scikit-learn, statsmodels) and SQL.
- Select appropriate techniques across regression, classification, clustering, time-series, deep learning, and causal inference, and justify trade-offs to stakeholders.
- Own the statistical soundness of analytical deliverables, including assumptions, uncertainty quantification, and limitations.
- Establish reproducible analytical workflows and promote good practice in code quality, documentation, and version control within the team.
- Present findings and recommendations to senior, often non-technical, stakeholders through clear narratives and visualizations that drive decisions.
- Review and provide technical feedback on the analytical work of data scientists, raising the overall standard of the team. Mentor and coach junior and mid-level data scientists, supporting their technical and professional growth.
- Partner with machine-learning and AI engineers to hand off validated models for productionization and to define monitoring and success metrics.
- Contribute to proposals, scoping, and effort estimation for new data science engagements.
- Bachelor's degree in Statistics, Mathematics, Computer Science, Data Science, or a related quantitative field; Master's or PhD preferred.
- Deep proficiency in Python for analysis and the scientific stack (pandas, NumPy, scikit-learn, statsmodels) and strong SQL.
- Strong foundation in statistics and experimental design, with command of a broad range of modeling techniques.
- Hands on experience applying deep learning and neural network architectures using frameworks such as TensorFlow or PyTorch.
- Experience designing and interpreting experiments and translating results into business recommendations.
- Demonstrated ability to frame business problems and communicate analytical results to executive and non-technical audiences.
- Experience mentoring analysts or data scientists and setting analytical standards or methodology.
- Strong understanding of the end-to-end data science lifecycle, including data quality, validation, and model handoff.
- Ability to manage multiple workstreams and stakeholders simultaneously.