Ops Wfm Forecasting Analyst Intern F - m H/F

Allianz Partners; Saint-Ouen-sur-Seine; Presencial; EUR 12.000 - 17.000; Jornada completa

Descripción de la vacante

Allianz Partners is seeking a data-focused intern to support long-term forecasting for designated business lines in the Greater Paris area. You will work with time-series data, document assumptions, and help improve forecast accuracy using Python-based tools.

You will learn to run and maintain forecasting models (Python/Prophet) and collaborate with Data OPS to ensure data quality across platforms, while communicating findings in English to stakeholders.

Formación

• Master's level student in Mathematics, Applied Mathematics, Statistics, Data Science, CS or closely related field.
• Strong Python data analysis skills with optional ML basics (pandas, numpy, scikit-learn).
• Solid grounding in statistics, probability and time-series concepts.
• Working knowledge of Excel for analysis and stakeholder outputs.
• Clear written and spoken English; ability to explain technical findings to non-technical audiences.
• Curious, rigorous, detail-oriented and able to work independently and in a global team.

Responsabilidades

• Support production of long-term forecasts for specific units and lines of business.
• Help document assumptions and support reviews with Operations managers and capacity planners.
• Analyze data to identify trends, gaps and improve forecast accuracy.
• Learn and maintain forecasting model (Python/Prophet) for assigned country/line of business.
• Collaborate with Data OPS to keep data accurate across reporting platforms.
• Support data uploads into WFM tools and respond to ad-hoc requests.

Conocimientos

Time series analysis

Python for data analysis

Statistics & probability

English communication

Curiosity & initiative

Educación

Masters-level student (Bac +5) in Mathematics/Statistics/Data Science/CS

Herramientas

Python (pandas, numpy, scikit-learn)

Excel

SQL

Git/GitHub

Power BI

Prophet

Descripción del empleo

What you do

• Support the production of long-term volume forecasts for specific business units and lines of business, and help document the assumptions behind them for reviews with Operations managers and WFM capacity planners
• Contribute to regular reviews comparing actual volumes with forecasted volumes, helping to understand gaps and improve forecast accuracy over time
• Analyze data to spot trends, patterns and outliers, and help other WFM teams interpret the data correctly in their reports and planning
• Learn to run and maintain the forecasting model (Python / Prophet) for your assigned country or line of business, and contribute to improving its accuracy
• Collaborate with Data OPS and other cross-functional teams to keep data accurate and consistent across reporting platforms, and help document validation processes so work is transparent and reproducible
• Support the upload of validated data into WFM tools, and assist with ad-hoc analysis and reporting requests from stakeholders, with guidance from your team
• Work effectively in an environment shaped by artificial intelligence (AI), machine learning, data analytics and cloud-based tools, using insight responsibly and in line with our standards for data governance, security and ethical use

What you bring

• A Master's-level student (Bac +5) from a university, engineering school or business school, specialising in Mathematics, Applied Mathematics, Statistics, Data Science, Computer Science or a closely related field - you need enough quantitative background to work confidently with time series, forecasting and data validation
• Solid foundations in Python for data analysis and basic machine learning (e.g. pandas, numpy, scikit-learn, statsmodels) demonstrated through coursework or projects - you will be working with a Python-based forecasting model in production
• Good grounding in statistics and probability, ideally including time series concepts - this matters for understanding forecast assumptions, backtesting and accuracy metrics
• Working knowledge of Microsoft Excel (pivot tables, basic formulas, reporting) - still used widely for analysis, checks and stakeholder-facing outputs
• Clear written and spoken English, and the ability to explain technical findings to a non-technical audience - you will present forecasts and assumptions to Operations managers and planners
• A careful, rigorous way of working, with strong attention to detail and respect for deadlines - small errors in data or assumptions can have a big impact on staffing and service levels
• Curiosity and initiative: you're willing to ask questions, propose ideas, and improve processes, while being able to work both independently with guidance and as part of a global, multicultural team

Nice to have

• Familiarity with Prophet or similar forecasting libraries
• Basic SQL, and exposure to cloud or data platforms (AWS, Azure, GCP, Databricks, Snowflake)
• Basic knowledge of Git / GitHub (or equivalent) for version control and team collaboration
• Experience with BI / data visualization tools (e.g. Power BI)
• Interest in insurance or operations environments, and/or an additional language such as French

Internship details

• Start date: January 2027
• Duration: 6 months
• Location: Saint-Ouen (Greater Paris Region)

109140 Operations Student n.a. Allianz Partners Part-Time Temporary Allianz Group is one of the most trusted insurance and asset management companies in the world. Caring for our employees, their ambitions, dreams and challenges, is what makes us a unique employer. Together we can build an environment where everyone feels empowered and has the confidence to explore, to grow and to shape a better future for our customers and the world around us. At Allianz, we stand for unity: we believe that a united world is a more prosperous world, and we are dedicated to consistently advocating for equal opportunities for all. And the foundation for this is our inclusive workplace, where people and performance both matter, and nurtures a culture grounded in integrity, fairness, inclusion and trust. We therefore welcome applications regardless of ethnicity or cultural background, age, gender, nationality, religion, social class, disability or sexual orientation, or any other characteristics protected under applicable local laws and regulations. Great to have you on board. Let's care for tomorrow.