Machine Learning Intern
@ WillHire

Hybrid
€37,440
Hybrid
Intern
Posted 22 days ago

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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 expanding into the Data & Analytics vertical and are looking for curious, driven, and analytical minds to join our AI/ML Internship Cohort.

Role Overview

As a Machine Learning 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 working 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 (EDA) to uncover insights for recruitment strategies.
  • Work with time-series and cohort data for trend analysis.
  • Deploy statistical and ML algorithms like regression, clustering, and classification.
  • Communicate insights through clear visual and written presentations 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 and relational datasets.
  • Sound understanding of ML fundamentals and statistical concepts.
  • Ability to interpret data and present clear, actionable conclusions.
  • Strong communication skills, ownership mindset, and enthusiasm to learn.

Nice to Have (Bonus)

  • Experience with BI tools (Power BI, Tableau, Looker, Metabase).
  • Basics of cloud platforms (AWS, GCP, Azure) or Docker.
  • Previous 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 from experienced data scientists.
  • Internship Certificate & 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 note on your interest.
  • Technical Assessment – Test Python, SQL, EDA, or modeling approach.
  • Technical Interview – 45 minutes in-depth discussion on ML/statistics and problem-solving.
  • Managerial Interview – Evaluate communication skills, culture fit and motivation.
  • Offer – Selected applicants receive an internship offer with stipend details and project allocation.
  • Onboarding – Orientation, project assignment, and setup with tools and mentors.

Stipend

Minimum stipend starts at €18/hour, with potential for a higher rate based on performance during interviews.

Key skills/competency

  • Python
  • SQL
  • EDA
  • Machine Learning
  • Statistics
  • Data Cleaning
  • Data Visualization
  • Predictive Modeling
  • HR Analytics
  • Team Collaboration

How to Get Hired at WillHire

🎯 Tips for Getting Hired

  • Research WillHire's culture: Study mission, values, and recent news.
  • Customize resume: Highlight Python, SQL, and ML skills.
  • Prepare for technical tests: Practice EDA and model building exercises.
  • Showcase projects: Present GitHub or Kaggle work effectively.
  • Leverage mentorship: Use provided resources and recommendations.

📝 Interview Preparation Advice

Technical Preparation

Review Python libraries and coding exercises.
Practice SQL queries on sample datasets.
Work on EDA and predictive model projects.
Study statistical methods and ML algorithm basics.

Behavioral Questions

Prepare examples of teamwork and communication.
Discuss problem-solving in data projects.
Explain handling deadlines and feedback.
Share experiences with project challenges.

Frequently Asked Questions