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Ops WFM Forecasting Analyst Intern F/M

Saint-Ouen, Seine-Saint-Denis, France

Actively hiring, first seen by us 10 days ago
Pay
Pay not listed
Work setting
Not stated
Employment
Temporary — employment source
Employment type in the employer’s posting: Temporary
From the employer’s posting

Before you apply

New graduates
For current studentsJob title: Ops WFM Forecasting Analyst Intern F/M

What you’ll work on

Full posting
  • Support long-term volume forecasts and document their assumptions

  • Compare actual volumes with forecasts to improve accuracy

  • Analyze data for trends, patterns and outliers

  • Learn to run and maintain the Python/Prophet forecasting model

  • Help validate data and upload it into WFM tools

From the employer’s posting
Support the production of long-term volume forecasts for specific business units and lines of business, and help document the assumptions behind them
Contribute to regular reviews comparing actual volumes with forecasted volumes
Analyze data to spot trends, patterns and outliers
Learn to run and maintain the forecasting model (Python / Prophet)
Support the upload of validated data into WFM tools

What you’ll bring

All qualifications

Core experience

  • Familiarity with Prophet or similar forecasting libraries
  • 109140 | Operations | Student | n.a.
  • Experience with BI / data visualization tools (e.g.
Qualification wording
Familiarity with Prophet or similar forecasting libraries
109140 | Operations | Student | n.a. | Allianz Partners | Part-Time | Temporary
Experience with BI / data visualization tools (e.g. Power BI)
Education & alternatives
This internship sits in Global Operations (COO), within the Workforce Management (WFM) team. Their job is to predict future operations volumes so the business can staff correctly and keep service levels under control. We’re looking for a Master’s-level student who enjoys working with data and Python, and wants to see how forecasting is used in a real global insurance operations environment. If you like turning messy historical data into usable insight, checking whether a forecast really “holds up” against actuals, and explaining what you’ve found in clear language, this will be a good fit. You’ll work closely with experienced forecasters and the Data OPS team, so you’ll learn how production forecasting actually works end-to-end, not just in theory.
We’re looking for: - 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
Apply at Allianz Insurance

Posting record

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Actively hiringFirst seen by us 10 days ago, still listed, and not a repost of an earlier posting.

Status in our records
Live
First seen by us
Nov 3, 202610 days ago
Recorded sightings
11
Last seen by us
Nov 13, 2026

These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.

How this job compares

Time since we first saw it

Day 10

Similar jobs usually come down between day 5 and day 43.

Well past Allianz Insurance’s usual: its jobs come down by about day 3.

From 14,545 similar postings that came down, and 421 of Allianz Insurance’s. Coming down is not always a hire.

Allianz Insurance is hiring

65 open jobs

Open jobs by the week we first saw them, last 12 weeks.

  • Analyst hiring is picking up
  • Takes jobs down quickly
  • 5% of open jobs were reposted
Hiring record

See how this role fits your experience

Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.

  • Python
  • SQL
  • AWS
  • Azure
  • Databricks
  • Excel
  • GCP
  • Power BI
  • Snowflake
  • NumPy
  • pandas
  • scikit-learn
Where the posting names them
This internship sits in Global Operations (COO), within the Workforce Management (WFM) team. Their job is to predict future operations volumes so the business can staff correctly and keep service levels under control. We’re looking for a Master’s-level student who enjoys working with data and Python, and wants to see how forecasting is used in a real global insurance operations environment. If you like turning messy historical data into usable insight, checking whether a forecast really “holds up” against actuals, and explaining what you’ve found in clear language, this will be a good fit. You’ll work closely with experienced forecasters and the Data OPS team, so you’ll learn how production forecasting actually works end-to-end, not just in theory.
- 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
- 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
- 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
- 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
- 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

Your next step

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Demand for Analyst roles

New Analyst postings at the employers we follow are up 23% over the last 6 weeks compared with the 6 before.

Weekly new Analyst postings at the same employers, from the week of August 17 to the week of November 2. The fewest in a week was 993, the most 1,515, and the latest week had 1,515.
August 17Last week

Each bar is one week of new postings. The darkest bars are the last 6 weeks.

Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.

About the role

We file this job under Analyst, by the work the posting describes.

Analysts answer business questions with data. They pull numbers with SQL, build reports and dashboards, and explain what changed and why to the people making decisions.

Most postings ask for SQL, spreadsheets, a dashboard tool such as Tableau or Power BI, and clear writing.

Unlike a data scientist, an analyst mostly explains what happened rather than building models that predict what will.

Job description

View original posting ↗

 

This internship sits in Global Operations (COO), within the Workforce Management (WFM) team. Their job is to predict future operations volumes so the business can staff correctly and keep service levels under control.

 

We’re looking for a Master’s-level student who enjoys working with data and Python, and wants to see how forecasting is used in a real global insurance operations environment. If you like turning messy historical data into usable insight, checking whether a forecast really “holds up” against actuals, and explaining what you’ve found in clear language, this will be a good fit.

 

You’ll work closely with experienced forecasters and the Data OPS team, so you’ll learn how production forecasting actually works end-to-end, not just in theory.

 

 

What you do

 

You will:

 

  • 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

 

We’re looking for:

 

  • 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 (not mandatory, but a plus):

 

  • 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)

 

How we hire

 

We hire directly. Allianz Partners does not accept unsolicited CVs or approaches from agencies. We only work with partners on our approved supplier list, under contract. Any unsolicited submissions will not be considered.

 
 
 

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.

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