Data Science Career Path in Finance Infographic | payroll structure chart

Career infographic titled "Data Science Career Path in Finance". INTERVIEW QUESTIONS deck layout, editorial LinkedIn-magazine illustration, infographic-magazine style, growth green & gold palette, clean finance-themed visuals, modern cards and panels, subtle chart/grid motifs, no real company logos as endorsements. Create 7 labeled sections in English with interview-focused, concrete, actionable tips for progressing through a data science career in finance. Section 1: "Career Ladder" showing Junior Data Analyst → Data Scientist → Senior Data Scientist → Lead Data Scientist → Head of Data Science, with 1-line responsibility summaries. Section 2: "Core Technical Questions" with prompts on SQL, Python, machine learning, feature engineering, model validation, and time-series forecasting in finance. Section 3: "Finance Domain Questions" with prompts on credit risk, fraud detection, pricing, forecasting, regulatory awareness, and KPI interpretation. Section 4: "Case Study Challenge" with a practical finance scenario, expected approach, evaluation criteria, and a do/don't callout. Section 5: "Portfolio & Projects" with actionable tips to present notebooks, dashboards, model impact, documentation, and measurable outcomes. Section 6: "Skills to Build" with hard skills and soft skills, including communication, stakeholder management, model explainability, and decision-making. Section 7: "Example Salary Progression" as clearly labeled illustrative example only, using GBP: Junior Data Analyst £35,000 example, Data Scientist £55,000 example, Senior Data Scientist £80,000 example, Lead Data Scientist £105,000 example, Head of Data Science £140,000 example; show as simple bars or card metrics with explicit "example" labels. Include concise do/don't callouts across the deck, such as do quantify business impact, do explain assumptions, don't overclaim model performance, don't ignore bias or governance. Visually imply payroll structure chart search intent through abstract compensation bands, level mapping, and compensation flow cues, but render no on-image text for that search phrase. Inclusive, non-discriminatory framing. All text MUST be written in English (array). Every heading, label, caption, legend and metric name in the image must be in English — not English. Spell each English word correctly using English characters and diacritics. Numbers stay as digits, no real company logos as recommendations, no watermarks No real company logos as endorsements. Illustrative salary numbers labeled as example. No discriminatory framing.