AI Developer & Data Analyst

شرح موقعیت

Digikala is looking for an AI Developer & Data Analyst to join its data and analytics team. This hybrid role combines business and product analytics with the design and development of AI-powered agents and analytical applications. The successful candidate will work closely with product, business, data, and engineering teams to turn complex business questions into reliable analytical insights and scalable AI solutions. The role is suitable for someone who is comfortable investigating data, developing analytical models, and building AI agents that can interact with data sources, tools, and internal systems. Key Responsibilities Data Analytics Analyze business, product, customer, operational, and marketplace data to identify trends, opportunities, risks, and root causes. Translate business questions into clear analytical frameworks, metrics, hypotheses, and measurable outcomes. Develop and maintain dashboards, reports, KPI-monitoring solutions, and automated analytical workflows. Conduct exploratory analysis, funnel analysis, segmentation, cohort analysis, root-cause analysis, and performance evaluation. Define and document business metrics, calculation logic, dimensions, data sources, and quality requirements. Collaborate with business and product stakeholders to interpret analytical findings and recommend actionable next steps. Design and evaluate experiments, including A/B tests and causal analysis, where appropriate. Validate data accuracy and investigate inconsistencies across dashboards, databases, and analytical systems. Communicate findings through clear reports, presentations, visualizations, and executive-level summaries. AI Agent Development Design, develop, and maintain AI agents that support analytical and operational use cases. Build agentic workflows capable of planning tasks, querying data, using tools, collaborating with other agents, and generating structured outputs. Integrate large language models with databases, APIs, metadata platforms, business applications, and internal knowledge sources. Develop reliable tool-calling mechanisms for SQL execution, metric retrieval, document search, reporting, and operational actions. Implement multi-agent orchestration, task routing, context management, memory, and agent-to-agent delegation. Build retrieval-augmented generation systems using structured and unstructured organizational knowledge. Create evaluation frameworks for measuring agent accuracy, reliability, latency, cost, and business impact. Implement appropriate validation, observability, access control, and human-in-the-loop mechanisms. Improve prompts, tools, workflows, and system architecture based on evaluation results and user feedback. Ensure AI-generated insights are grounded in trusted data and clearly distinguish facts, assumptions, and recommendations. Required Qualifications Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, Mathematics, Industrial Engineering, or a related field. Strong proficiency in SQL and experience working with large-scale analytical databases. Strong Python programming skills and experience using data analysis libraries such as Pandas, NumPy, and related tools. Practical experience performing business, product, or operational analytics. Ability to structure ambiguous business problems and translate them into analytical or technical solutions. Understanding of statistical analysis, experimentation, hypothesis testing, and analytical modelling. Experience working with large language models and developing LLM-powered applications. Familiarity with prompt engineering, tool calling, structured outputs, RAG, embeddings, and vector search. Understanding of software engineering principles, including modular design, version control, testing, APIs, and code review. Strong communication skills and the ability to explain technical findings to non-technical stakeholders. Ability to work effectively across multiple teams in a fast-paced e-commerce environment. Preferred Qualifications Experience developing production-grade AI agents or multi-agent systems. Familiarity with frameworks such as LangGraph, LangChain, Semantic Kernel, or similar technologies. Experience with FastAPI, Docker, Git, CI/CD pipelines, and microservice architectures. Experience with ClickHouse, PostgreSQL, Microsoft SQL Server, or similar database technologies. Familiarity with workflow orchestration tools such as Airflow. Experience with BI and visualization tools such as Power BI, Metabase, Tableau, or similar platforms. Knowledge of e-commerce metrics such as conversion rate, orders, NMV/GMV, customer retention, cancellations, returns, fulfilment, pricing, and marketplace performance. Experience with causal inference, forecasting, anomaly detection, machine learning, or optimization. Familiarity with AI evaluation, monitoring, tracing, guardrails, and model cost optimization. Experience working with metadata platforms, semantic layers, metric stores, or data governance systems. Success in This Role Success in this position will be measured by: The quality, accuracy, and business impact of analytical insights. The adoption and usefulness of developed AI agents. Reduction in the time required to answer recurring business questions. Reliability and accuracy of AI-generated analyses and recommendations. Reusability of analytical and agentic components across different teams. Improvement in access to trusted metrics, data, and organizational knowledge. Effective collaboration with business, product, data, and engineering stakeholders.