Senior Data Scientist
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Key skills for this role
About the Role
Hyper Lychee Labs seeks a Senior Data Scientist for a 3-month contract in Riyadh, KSA. The role involves developing advanced analytical models for retail and corporate banking, requiring 8-20 years of experience and expertise in machine learning, statistical modeling, and Python.
Key Skills for This Role
Responsibilities
- Develop statistical, predictive, and machine learning models to solve business problems across banking functions
- Build forecasting, segmentation, optimization, and simulation models to support strategic decision making
- Conduct scenario analysis, stress testing, and Monte Carlo simulations
- Translate complex analytical findings into actionable business recommendations
- Collaborate with product, risk, finance, operations, and strategy teams to identify data driven opportunities
- Define and enhance data pipelines across core banking systems, CRM platforms, and enterprise data warehouses
- Ensure compliance with data governance, security, and regulatory requirements
- Partner with engineering teams to operationalize analytical models into production
Requirements
- Master's or PhD in Data Science, Statistics, Economics, Mathematics, Computer Science, or related quantitative discipline
- 8–20 years of experience in Data Science, Advanced Analytics, or Quantitative Modeling
- Strong expertise in regression, Bayesian modeling, machine learning, clustering, survival analysis, and time series forecasting
- Proficiency in Python, R, TensorFlow, PyTorch, and modern ML frameworks
- Proficiency in SQL and cloud based data platforms (Snowflake, BigQuery, Azure Synapse, etc.)
- Proven ability to deliver measurable business impact through data science initiatives
- Strong communication and stakeholder management skills
- Ability to work independently in a fast paced environment
Full Job Posting
Role Overview
- We are seeking a highly capable Senior Data Scientist to develop advanced analytical models, generate actionable business insights, and drive data driven decision making across retail, SME, and corporate banking functions.
- The ideal candidate is a hands on problem solver who thrives in a fast paced environment, can work with large and complex datasets, and has a proven track record of delivering business impact through statistical modeling, machine learning, forecasting, and optimization techniques.
Key Responsibilities
- Develop statistical, predictive, and machine learning models to solve business problems across banking functions.
- Build forecasting, segmentation, optimization, and simulation models to support strategic decision making.
- Conduct scenario analysis, stress testing, and Monte Carlo simulations where appropriate.
- Establish best practices for experimentation, model validation, deployment, and performance monitoring.
- Translate complex analytical findings into actionable business recommendations.
- Collaborate with product, risk, finance, operations, and strategy teams to identify opportunities for data driven improvements.
- Deliver insights that improve profitability, customer experience, operational efficiency, and business performance.
- Support executive stakeholders with data backed recommendations and performance analysis.
- Work alongside pricing and business specialists to provide analytical support for strategic initiatives.
- Define and enhance data pipelines across core banking systems, CRM platforms, and enterprise data warehouses.
- Ensure compliance with data governance, security, and regulatory requirements.
- Partner with engineering teams to operationalize analytical models and embed them into production environments.
Required Qualifications
- Master's or PhD in Data Science, Statistics, Economics, Mathematics, Computer Science, or a related quantitative discipline.
- 8–20 years of experience in Data Science, Advanced Analytics, or Quantitative Modeling.
- Strong expertise in: Regression, Bayesian modeling, machine learning, clustering, survival analysis, and time series forecasting.
- Optimization techniques including linear, quadratic, dynamic, and stochastic programming.
- Simulation methodologies, Monte Carlo analysis, and scenario modeling.
- Python, R, TensorFlow, PyTorch, and modern machine learning frameworks.
- SQL and cloud based data platforms such as Snowflake, BigQuery, Azure Synapse, or similar technologies.
- Proven ability to deliver measurable business impact through data science initiatives.
- Strong communication and stakeholder management skills with the ability to present findings to senior leadership.
- Ability to work independently and execute effectively in a fast moving, high performance environment.
Preferred Skills
- Experience within banking, financial services, fintech, or related industries.
- Exposure to pricing analytics, revenue optimization, or profitability analysis.
- Familiarity with Middle East banking markets and regulatory environments.
- Experience with BI and visualization tools such as Power BI, Tableau, or QlikSense.
- Knowledge of cloud native analytics and MLOps practices.
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