Senior Data Scientist

About the Role
We are looking for a data scientist who combines strong statistical and machine learning foundations with curiosity, independent judgment, and practical business sense.
You will work with messy, real-world data to solve problems in forecasting, optimization, customer intelligence, and operations across our e-commerce and agriculture technology businesses. Working with business and engineering teams, you will take problems from the initial question through investigation, experimentation, and delivery.
You thrive in a small, hands-on team where priorities evolve and problems are not always fully defined.
You take initiative, use AI tools and agents effectively, and own work through to a useful result.
What You Will Do
Modeling Forecasting and Optimization

• Independently develop modeling and optimization approaches suited to the business problem, available data, objectives, and practical constraints.
• Compare alternative methods against meaningful baselines, using evaluations that reflect how the results will be used.
• Investigate model failures and unexpected results, then propose and test improvements.
• Work with engineering teammates to bring useful models into production and translate results into decisions.

Data Science and Analytics

• Explore complex datasets, identify patterns and anomalies, and formulate hypotheses worth testing.
• Validate sources, metric definitions, and assumptions; investigate inconsistencies that could affect the answer.
• Design and analyze experiments, including A/B tests, using appropriate statistical methods.
• Test competing explanations, distinguish correlation from causation, and produce reproducible analyses with clear limitations.

AI and Applied Generative AI

• Use AI tools and agents to increase productivity, automate routine work, and accelerate research, analysis, coding, evaluation, and reporting through reusable workflows.
• Use AI to explore approaches and challenge assumptions, retaining responsibility for the reasoning and correctness of the result.
• Evaluate new tools, models, agents, and reusable skills on representative tasks, balancing quality, reliability, speed, and cost.
• Build and evaluate AI-powered tools for information retrieval, classification, extraction, and decision support.

Product and Business Collaboration

• Turn broad business requests into analytical questions, objectives, and success measures,
• questioning assumptions and identifying missing context.
• Communicate findings to technical and non-technical audiences, connecting the evidence, uncertainty, tradeoffs, and recommended action.
• Prioritize by expected business value, balancing rigor with speed and available resources.
• Incorporate stakeholder knowledge and material feedback, resolving open questions or agreeing how they will be handled.

Data Architecture Engineering and Model Operations
Your work will sometimes extend into data engineering, with an emphasis on architecture, reusable systems, and automated operations.

• Design reusable datasets, features, feature-store structures, and pipelines with clear definitions and traceable sources.
• Take selected components from design through implementation, working with engineering teammates where appropriate.
• Build in quality checks, automated evaluation, and monitoring to detect data failures, model drift, and performance degradation and inform retraining or revision.
• Make practical architecture decisions that balance reliability, flexibility, cost, and maintenance; document systems so teammates can support them.

Example Projects
Depending on business priorities, you may work on:

• Forecasting and planning: improve demand forecasts and help buyers understand changes, uncertainty, and inventory implications.
• Optimization: recommend replenishment quantities, inventory allocations, or resource plans within business constraints.
• Customer intelligence: develop segmentation, recommendation, or retention models for e-commerce and SaaS businesses.
• Agriculture technology: build yield, harvest, or operational planning tools.
• AI-powered decision support: combine business data, information retrieval, and agent workflows to help users investigate questions and act.

Required Qualifications

• Demonstrated ability to independently lead substantive data science work, from an ambiguous business question through investigation, evaluation, and delivery. We assess seniority through analytical judgment, ownership, and results rather than a fixed number of years.
• Strong foundations in statistics, probability, experimental design, and machine learning, with practical experience in forecasting or optimization.
• Strong Python and SQL skills, including experience with common data analysis and machine learning libraries.
• Experience working with large datasets and cloud-based data platforms.
• Demonstrated use of AI tools and agents in substantive analytical or development work, including validating their outputs and explaining the reasoning.
• Natural curiosity and self-directed learning. You actively explore new methods, tools, and AI capabilities, test their practical value, and apply what proves useful. You investigate unfamiliar problems and unexpected results without waiting to be directed.
• Clear written and verbal communication with technical and non-technical audiences.
• A degree in a quantitative discipline, or equivalent practical experience.

Why Join Us?

• AI Playground: Work in an environment where your AI knowledge isn't just welcomed-it's celebrated. Test and deploy AI solutions that make a real impact.
• Growth Opportunities: Mentorship from senior IT pros, access to online training (e.g., Udemy), and a clear path to roles like Systems Admin or AI Specialist.
• Young & Dynamic Team: Collaborate with peers who share your passion for tech and innovation.
• Perks: Competitive salary, health benefits and specific company perks, e.g., "free coffee," "free buffet lunch", "yoga" , "football" ..etc...