Senior Analyst- Quantitative Risk

Role Overview

This role sits where business strategy, engineering, and data meet. You will turn large volumes of raw information into sharp evaluations that guide major decisions, from operational fixes to long-term strategic bets. Most days involve a blend of writing analytical code, testing hypotheses, and sitting with senior leaders to make sure they understand not just what the numbers say, but why they matter and what to do about them. Your work exists to keep the organization honest by grounding choices in evidence rather than assumption.

Key Responsibilities

• Construct end-to-end analytical studies that unpack complex business problems into testable hypotheses, measurable variables, and clear conclusions.
• Design and run experiments such as A/B tests, regression analyses, and causal evaluations to assess the likely outcome of new strategies before they are fully launched.
• Translate vague, high-level concerns from leadership into precise analytical questions and rigorous quantitative methods.
• Develop interactive dashboards and reporting tools in platforms like Tableau, Looker, or Power BI that give stakeholders a real-time view of performance against KPIs.
• Audit existing business workflows and data signals to pinpoint drags on productivity, revenue, or customer satisfaction, then recommend realistic improvements based on findings.
• Partner with software and data engineering teams to refine data pipelines, improve data collection, and make sure analysis is built on reliable foundations.
• Present findings and recommendations in a way that adapts to each audience—detailed enough for technical reviewers, yet concise and compelling for executives making the final call.

Requirements & Qualifications

• Bachelor's or Master's degree in Data Science, Computer Science, Business Analytics, Statistics, Mathematics, Economics, or another field centered on quantitative reasoning.
• At least 4 years of professional experience in business intelligence, data analytics, decision science, or strategic evaluation, with demonstrated impact on company performance or operational change.
• Strong command of SQL and Python: you should be comfortable extracting and joining large data sets, building predictive or statistical models, automating recurring tasks, and working through ambiguous data challenges without constant supervision.
• Solid practical grounding in applied statistics, including experiment design, hypothesis testing, regression methods, cohort and trend analysis, root cause diagnostics, and translating statistical findings into business language.
• Familiarity with machine learning principles and predictive evaluation, plus the judgment to choose an appropriate approach when a simpler model will outperform a complex one.
• Hands-on experience with data visualization and BI tools such as Tableau, Power BI, or Looker, evidenced by dashboards that drive decisions rather than simply decorate a screen.
• Sharp business intuition: a knack for noticing where operational bottlenecks form, asking the right questions before running the numbers, and crafting recommendations that improve workflows or unlock new opportunities.
• Excellent communication skills, including the ability to lead data-focused conversations with both technical peers and non-technical stakeholders, and a track record of making analysis feel actionable rather than academic.