Data Analyst - Revenue and Pricing
Data Analyst - Revenue and Pricing
Date
Job ID
R18820
Country
France
Job City
Paris
Job Family
Data analysis
Job Type
Employee
Job Sub Type
Permanent
Key Responsibilities:
Data Analysis and Pricing Models:
- Develop, maintain, and improve pricing models based on large datasets and financial metrics.
- Conduct detailed analysis of market trends, customer data, and financial metrics to inform pricing decisions.
- Provide insights into the impact of pricing strategies on revenue, customer retention, and profitability - .
Financial Reporting & Forecasting:
- Generate financial reports related to pricing strategies, including revenue forecasting and cost analysis, supporting budget processes.
- Communicate findings to senior management and provide actionable recommendations.
Collaboration with Internal Teams:
- Work closely with product, finance, and sales teams to align pricing strategies with overall business objectives.
- Ensure pricing structures comply with regulatory standards and risk management protocols.
Automation & Process Improvement:
- Support the implementation of pricing automation tools and technologies to enhance efficiency and accuracy.
- Identify areas for process improvement and help develop innovative pricing models and strategies.
Required Skills & Qualifications:
- Educational Background:
Bachelor's degree in Finance, Economics, Data Science, Mathematics, or a related field preferrable. A Master’s degree or professional certification (e.g., CFA, FRM) is a plus. - Technical Skills:
- Proficiency in Excel is essential and in data analytics tools and programming languages such as Python, SQL, and Power BI is a distinct advantage.
- Experience with pricing strategies.
- Previous experience with financial metrics.
- Experience:
- Preferred minimum of 2 years of experience in a data analytics, financial analysis, or pricing role, preferably within the financial services sector.
- Analytical & Communication Skills:
- Strong analytical thinking with attention to detail.
- Ability to interpret complex datasets and present findings clearly to non-technical stakeholders.
- Fluency in English is essential, with French or Portuguese an advantage; an with good written and verbal communication skills