AI ML Intern @ WillHire
placeHybrid
attach_money €39,520
businessHybrid
scheduleIntern
Posted 24 days 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. Now expanding into the AI ML vertical, WillHire is looking for curious, driven, and analytical minds to join the AI ML Internship Cohort.
Role Overview
As an AI ML Intern at WillHire, you will collaborate with 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 working on real business datasets to build production-ready analytics models and dashboards.
Key Responsibilities
- Collect, clean, analyze, and transform structured & semi-structured HR and recruitment datasets.
- Build predictive models for talent forecasting, attrition risk, and candidate success scores.
- Develop data visualizations, dashboards, and reports using Python, SQL & BI tools.
- Perform exploratory data analysis to uncover insights that inform recruitment strategies.
- Work with time-series & cohort data for trend analysis and performance metrics.
- Deploy statistical and ML algorithms (regression, clustering, classification) in scalable pipelines.
- Communicate findings and recommendations with clear visual and written formats to stakeholders.
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 – supervised and unsupervised learning methods.
- Strong statistics foundation including 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 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
- EDA
- Data Cleaning
- Machine Learning
- Data Visualization
- Statistical Analysis
- Predictive Modeling
- BI Tools
- Communication
How to Get Hired at WillHire
🎯 Tips for Getting Hired
- Customize Your Resume: Highlight relevant AI, ML, and data skills.
- Showcase Portfolio: Include GitHub and project links.
- Research WillHire: Understand their approach to talent acquisition.
- Prepare for Assessments: Practice Python, SQL, and data analysis.
📝 Interview Preparation Advice
Technical Preparation
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Review core Python libraries.
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Practice SQL queries.
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Study ML algorithms.
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Work on data cleaning challenges.
Behavioral Questions
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Describe a challenging project.
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Explain teamwork experiences.
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Discuss time management strategies.
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Highlight learning moments.
Frequently Asked Questions
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