AI/ML Intern @ WillHire
placeHybrid
attach_money €39,520
businessHybrid
scheduleIntern
Posted 5 hours ago
Your Application Journey
Interview
Email Hiring Manager
****** @willhire.com
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Job Details
About WillHire
WillHire is a modern staffing and talent acquisition platform helping leading organizations find exceptional talent. We’re now expanding into the AI/ML vertical and are looking for curious, driven, and analytical minds to join us as part of our AI/ML Internship Cohort.
Role Overview
As an AI/ML Intern at WillHire, you will collaborate with our engineering and strategy teams to design data-driven solutions that power smarter hiring, workforce planning, and operational decision-making. This is a hands-on role where you’ll work on real business datasets to build production-ready analytics models and dashboards.
Key Responsibilities
- Collect, clean, analyze, and transform HR and recruitment datasets.
- Build predictive models for talent forecasting and attrition risk.
- Develop data visualizations, dashboards, and reports using Python, SQL, and BI tools.
- Perform exploratory data analysis to uncover recruitment insights.
- Handle time-series and cohort data for trend and performance analysis.
- Deploy statistical and ML algorithms in scalable pipelines.
- Communicate findings and recommendations to stakeholders clearly.
Requirements
- Pursuing or recently completed B.Tech/BE/M.Tech/MSc in Data Science, Computer Science, Statistics, or related fields.
- Proficiency in Python and core libraries (Pandas, NumPy, Matplotlib/Seaborn, Scikit-learn).
- Familiarity with SQL for querying relational datasets.
- Sound understanding of ML fundamentals including supervised/unsupervised learning methods.
- Strong statistics foundation covering distributions, hypothesis testing, and probability.
- Ability to interpret data, derive insights, and present conclusions clearly.
- Strong communication skills, ownership mindset, and enthusiasm to learn.
Nice to Have (Bonus)
- Knowledge of BI tools (Power BI, Tableau, Looker, Metabase).
- Basics of cloud platforms (AWS, GCP, Azure) or Docker.
- Prior exposure to HR analytics or recruitment datasets.
What You’ll Get
- Practical exposure to solving real-world data problems in HR Tech.
- Experience working on high-impact product features used by recruiters and hiring managers.
- 1:1 mentorship by experienced data scientists and access to premium resources.
- Internship Certificate and Letter of Recommendation upon successful completion.
- Opportunity for a Pre-Placement Offer (PPO) at WillHire or client companies.
Hiring Process
- Online Application: Submit CV, GitHub/Kaggle links, and a brief note on your interest and experience.
- Technical Assessment: Assignment to test Python, SQL, EDA, or modeling approach.
- Technical Interview: In-depth discussion (45 mins) on your ML/stats understanding and problem-solving.
- Managerial Interview: Evaluate communication skills, culture fit, and motivation.
- Offer: Selected applicants receive the internship offer with stipend details and project allocation.
- Onboarding: Orientation, project assignment, and setup with tools and mentors.
Stipend
The minimum stipend starts at 19 euro/hour, with the possibility of a higher rate based on interview performance.
Key skills/competency
- Python
- SQL
- Machine Learning
- Data Analysis
- Predictive Modeling
- Data Visualization
- Exploratory Data Analysis
- Statistical Analysis
- HR Analytics
- Mentorship
How to Get Hired at WillHire
🎯 Tips for Getting Hired
- Customize your resume: Highlight ML projects and relevant coursework.
- Showcase your portfolio: Link GitHub or Kaggle projects.
- Practice technical skills: Prepare for Python, SQL, and modeling assessments.
- Prepare for interviews: Review ML fundamentals and case studies.
📝 Interview Preparation Advice
Technical Preparation
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Review Python data libraries and syntax.
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Practice SQL queries on sample databases.
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Study ML algorithms and statistical methods.
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Work on data visualization using BI tools.
Behavioral Questions
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Explain a challenging project experience.
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Describe teamwork and conflict resolution methods.
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Share examples of self-driven learning moments.
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Discuss handling feedback in a project.
Frequently Asked Questions
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