Data Science Expert

Overview

• What you ll build:
• In this role, you'll turn data into decisions by identifying high-value business problems, framing them analytically, and delivering models and insights that improve customer outcomes and product performance.
• You'll work across exploratory analysis, statistical modeling, experimentation, forecasting, segmentation, anomaly detection, causal analysis, and machine learning, while partnering closely with product, engineering, sales, operations, or finance stakeholders.
• Your work will not stop at model accuracy: you'll define success metrics, quantify impact, translate findings into business actions, and help teams prioritize based on value, risk, and feasibility.
• You ll also contribute to AI-driven analytics using NLP, embeddings, and LLM-assisted workflows for summarization, classification, knowledge extraction, and decision support.
• The role requires someone who can connect business context with rigorous analysis, explain trade-offs clearly, and influence roadmap decisions with evidence.
• What you bring:
• Strong command of Python and SQL for data extraction, statistical analysis, feature engineering, model development, and analytical automation
• You have a deep understanding of statistics and probability, including hypothesis testing, experiment design, A/B testing, regression, causal thinking, and interpreting uncertainty
• You have hands-on experience with a range of machine learning techniques, such as classification, forecasting, clustering, recommendation systems, anomaly detection, and propensity modeling
• You are skilled at framing business problems into measurable analytical questions, defining success metrics, and connecting model outputs to commercial or operational decisions
• You excel at building clear data narratives through dashboards, visualizations, presentations, and concise executive communication
• You demonstrate practical expertise in feature selection, model validation, error analysis, bias identification, and balancing trade-offs between accuracy and usability
• You have exposure to modern AI approaches, including NLP, generative AI, and LLM-assisted analytics (e.g., semantic search, text classification, summarization, prompt-based analysis)
• You are experienced with modern analytics and data platforms, such as notebooks, BI tools, cloud data warehouses, Spark, and large-scale data processing environments
• You have a strong understanding of data quality, lineage, governance, and the discipline required to produce trustworthy and reusable analyses