About ELCA
We are ELCA, one of the largest Swiss IT tribe with over 2,000 experts. We are multicultural with offices in Switzerland, Spain, France, Vietnam and Mauritius. As one of Switzerland's best employers of 2022, we are proud to rank among the top 20 preferred companies within the "Internet, Telecommunication and IT" category. Since 1968, our team of engineers, business analysts, software architects, designers and consultants provide tailor-made and standardized solutions to support the digital transformation of major public administrations and private companies in Switzerland. Our activity spans across multiples fields of leading-edge technologies such as AI, Machine & Deep learning, BI/BD, RPA, Blockchain, IoT and CyberSecurity.
Your role
Detecting trends in technology usage and skill demand is critical to fulfill a company’s requirements for talent growth. In this context, Artificial Intelligence has the potential to improve talent and workforce management strategies by providing advanced analytics on the profiles and competences currently present within the enterprise.
Studying and modeling the composition and evolution of the workforce in terms of skills, competences and compatibility to specific workloads and projects is a necessary first step towards AI-supported solutions for talent management.
The ELCA Data Science team wants to explore and evaluate applications of Graph-based approaches to model the skills and competences found within a skills and projects databases, to produce a talent map of an enterprise. One key activity of this exploration will be the creation of a dynamic Knowledge Graph allowing to submit queries and perform inference with Graph-ML models. To do so, you will reuse the outcomes of a previous internship, which focused on Knowledge Graph construction.
Based on this complex data product, you will investigate retrieval and prediction tasks, as well as visualizations that provide valuable analytical insights. Finally, you will explore the industrialization of your approach.
What you will learn:
You will be a Junior Data Scientist for the ELCA Data Science team, developing your skills in the Machine Learning domain under the guidance of experienced data scientists. You will tackle a concrete problem, with real-world enterprise data, helping ELCA in modeling and taking advantage of its knowledge bases.
Challenges:
To be successful, you will be in regular contact with your supervisors and find out about the needs and pain points in the company. You will perform data analyses, develop, and evaluate solution prototypes and communicate your results.
Objectives:
- Define and harmonize the collection of data from internal sources (CV database, AD & Organigram, Project database, CRM).
- Propose a data modeling approach for the construction of a comprehensive enterprise talent map (graph), complete with all the entities and relationships of interest.
- Evaluate the structure and performance of the talent map on information retrieval and inference tasks: predict the best skillsets/hires to complement a team, determine the best profile matches to projects, retrieve the most relevant resources according to queries.
- Explore visualization approaches based on the current and past structure of the talent map (evolution of skills over time, relationship between skills and career trajectory, clustering of competences according to the organizational structure).
Evaluate and mitigate problems related to principles of responsible AI usage, such hidden biases and removal of personal identifiable data.
Our offer
- A dynamic work and collaborative environment with a highly motivated multi-cultural and multiples international sites team
- Personal development through training and coaching
- A flat hierarchy and a culture of collaboration across all disciplines
- The chance to make a difference in peoples’ life by building innovative solutions
- High innovation and research backed up by collaboration with universities like EPFL
- Various internal coding events (Hackathon, Brownbags), see our technical blog
- Monthly After-Works organized per locations
- Good life balance (41 working hours per week and possibility to work 2 days per week from home)
Your profile
Knowledge
- Data modeling and engineering
- Data analysis and visualization
- Machine Learning & Natural Language Processing
- Knowledge Graphs and Graph ML is a plus
Skills
- Solid Python skills, good software engineering practices
- ML/DL frameworks, Data Viz libraries
