Data Science Lead

As a Data Science Lead, you will be at the forefront of solving complex, real-world problems that significantly impact millions of users in Iran. In this role, you will operate as a true product leader, challenging assumptions and utilizing advanced machine learning and statistical techniques to shape the strategic direction of our products.

Working at the intersection of engineering, product, and business, you will act as a thought partner to stakeholders. You will navigate high levels of ambiguity, guiding the technical vision and taking full accountability for end-to-end data projects, from initial problem formulation to highly scalable, deployed solutions.


What You Will Do

• Product Leadership & Strategy: Proactively identify opportunities to leverage data science to drive business value, translating ambiguous business challenges into well-defined ML and analytical formulations.
• End-to-End Modeling: Architect, develop, and deploy robust statistical and machine learning models, ensuring they scale effectively to large datasets in production environments.
• Experimentation at Scale: Design and evaluate complex experiments (e.g., A/B testing, causal inference) to measure the true causal impact of models and product changes on user experience.
• MLOps & Reliability: Define rigorous model performance metrics, implement continuous monitoring systems, and champion MLOps best practices to maintain model reliability and prevent drift over time.
• Technical Excellence: Elevate the technical baseline of the data team by reviewing code, designing system architectures, and researching state-of-the-art algorithms.
• Mentorship: Mentor and coach junior to mid-level data scientists, fostering a culture of continuous learning, rigorous scientific validation, and high engineering standards.

Minimum Qualifications

• 6+ years of industry experience in Data Science, Machine Learning, or Applied Statistics.
• Bachelor’s degree in Computer Science, Statistics, Mathematics, Physics, Engineering, Economics, or a related highly quantitative field.
• Expert-level proficiency in Python and SQL.
• Solid hands-on experience with ML/DL frameworks (e.g., Scikit-learn, PyTorch, or TensorFlow) and data manipulation libraries.
• Proven track record of leading end-to-end machine learning projects from conception to production.
• Strong business acumen (Product Sense) with the ability to connect algorithmic performance to tangible business KPIs (e.g., ROI, retention, conversion).
• Exceptional communication skills to translate highly technical concepts to non-technical executive stakeholders and drive cross-functional alignment.

Preferred Qualifications

• Experience working with distributed computing and big data tools (e.g., PySpark).
• Hands-on experience deploying models using modern model-serving frameworks (e.g., Triton, MLflow).
• Familiarity with cloud infrastructure and containerized environments (Docker, Kubernetes).
• Advanced knowledge of causal inference, advanced experimentation setups, or reinforcement learning.