Senior Data Scientist
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Key skills for this role
About the Role
malomatia is seeking a Senior Data Scientist with 6-10 years of experience to lead advanced analytics and statistical modeling projects. The role requires deep command of statistics, machine learning, and experimental design, and the ability to translate business questions into data science problems.
Key Skills for This Role
Responsibilities
- 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
Requirements
- 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
- 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
Full Job Posting
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.
Nice to have
- 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.
Responsibilities
- 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.
Qualifications
- 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.
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