Advanced Technical Analyst

(JD) Analytics - Business Analyst

Role Summary:

This role is responsible for turning business questions into structured analyses, meaningful metrics, measurable KPIs, actionable insights, and decision-

ready recommendations. The role combines traditional analytics, statistical methods, and business translation to improve planning, performance, and

process decisions across the function. It contributes to team and business goals by identifying opportunities, testing hypotheses, and enabling

stakeholders to act on reliable, well-explained data. The role also helps determine how a problem should be solved using data, whether through reporting,

BI, traditional analytics, or advanced data science methods.

Key Responsibilities:

• Lead end-to-end analytical problem solving by translating business questions into hypotheses, analysis plans, and decision-ready
• recommendations.
• Assess business problems and determine the right data approach, including reporting, BI, traditional analytics, or data science.
• Own KPI design, metric definitions, dashboards, reports, and recurring insight packs that improve visibility into business performance.
• Analyze large structured and semi-structured datasets using SQL, Python, Excel, and BI tools to identify trends, risks, anomalies, root causes,
• and opportunities.
• Apply statistical methods such as hypothesis testing, confidence intervals, segmentation, cohort analysis, and experimentation design to evaluate
• initiatives and quantify impact.
• Design and assess A/B tests, interpret statistical and practical significance, and communicate business implications clearly.
• Support basic predictive analysis and time-series forecasting where relevant, including simple model development, validation, and scenario
• analysis.
• Collaborate with business, product, operations, engineering, and data teams to improve data quality, instrumentation, analytical workflows, and
• action tracking.
• Ensure analytical outputs are accurate, reproducible, and well documented through validation checks, notebook discipline, version control, and
• clear metric logic.

Required Qualifications:

Experience:

• Experience in data analysis, business analysis, business intelligence/reporting, or techno-functional analytics roles.
• Hands-on experience with SQL, Python, advanced Excel, and one BI platform such as Power BI or Tableau.
• Experience working with Jupyter notebooks, GCP, and BigQuery.
• Proven experience in exploratory data analysis, reporting, dashboarding, data validation, and several of the following: statistical analysis,
• hypothesis testing, A/B testing, segmentation, cohort analysis, predictive modelling, time-series forecasting, or experimentation design.
• Experience partnering with stakeholders to frame business questions, define KPIs and success metrics, and translate findings into practical
• actions.
• Experience working cross-functionally and translating business needs into analytical or technical deliverables.

Skills:

• Strong technical skills in SQL, Python, Excel, data wrangling, dashboarding, statistical methods, data visualization, and basic ML workflows.
• Working knowledge of statistical and analytical libraries such as scikit-learn and statsmodels for basic modelling and analysis.
• Functional expertise in problem structuring, metric design, root-cause analysis, forecasting, experimentation, and causal reasoning.
• Strong communication and stakeholder management skills, with the ability to explain methods, assumptions, and recommendations clearly to non-
• technical audiences.
• Analytical mindset, high attention to detail, data-quality discipline, and comfort working independently in ambiguous environments.

AI Skills (Mandatory):

• Proficiency in leveraging AI tools for daily engineering tasks to enhance productivity, optimize effort, and ensure cost-aware AI assistance.
• Familiarity with Retrieval Augmented Generation (RAG) architectures, including semantic layers for data retrieval and grounding Large Language
• Model (LLM) responses.
• Conceptual understanding of LLM-based applications (e.g., chatbots, Q&A systems), encompassing prompt engineering, context management,
• and response generation.
• Exposure to agentic and multi-agent AI systems, including agent roles, tool utilization, memory management, and workflow orchestration.
• (Good to have) Practical experience with LLM application frameworks (e.g., LangChain, LangGraph, or Google GenAI tools) for prototyping and
• integrating AI-driven solutions.

Nice to Have:

• Familiarity with Git-based workflows, reusable notebook practices, and collaboration with data science or ML teams.
• Exposure to advanced analytics techniques such as forecasting, causal inference, or uplift measurement.
• Domain experience in retail, grocery, e-commerce, operations, pricing, supply chain, marketing, or customer analytics.