Technical Mentor Data Science for Social Good (per remote)

Institution
Faculty of Mathematics, Informatics and Statistics - Chair of Statistical Learning and Data Science
Start date
24.05.2021
Application deadline
28 Feb 2021

Within the Munich Center for Machine Learning, the Chair of Statistical Learning and Data Science at the Ludwig-Maximilians-Universität in Munich, led by Prof. Dr. Bernd Bischl, in close cooperation with the University of Warwick, and supported by The Alan Turing Institute and the Data Science for Social Good (DSSG) Foundation is looking for full-time Data Science Technical Mentors that would like to join the DSSGx UK Summer Project.

The 12-week project-based training programme, is designed to give aspiring data scientists a strong skill set for solving real-world problems and an understanding, excitement, and passion for solving problems with social impact. Working closely with governments and nonprofits, fellows take on real-world problems in education, health, international development, and more. This year, due to COVID-19 the programme will run entirely online.

Tasks and responsibilities

The Data Science Technical Mentors (TMs) are a critical component of DSSGx. TMs work with dedicated teams of Fellows and provide hands-on technical mentoring and data science expertise to the projects. Each TM is teamed up with a Project Manager who leads the relationship with the project partner as well as making sure the team is moving forward. Mentors will also help teach workshops and tutorials over the summer and are an integral part of the organizing team.

An ideal TM is an experienced data scientist with strong technical skills, practical experience, with an interest in mentoring students to learn real-world skills and make a social impact. Typically, TMs have worked in industry for a significant amount of time; working in areas such as Computer Science, Machine Learning, Social Science, Statistics, and related areas, and have experience working on data science problems.

Your duties and responsibilities:

  • Project Lead on 2-3 projects
  • Lead typically 2 or 3 teams of Fellows (3-4 people per team)
  • Technical adviser
  • Teach workshops and tutorials
  • Develop some material for the taught components

Requirements

Essential:

  • Competent methodological background in applied ML and applied data science
  • Experience with either data science / machine learning toolkits in Python (Panda, NumPy, Matplotlib, Scikit-Learn, Beautiful Soup) or R (mlr, caret, tidyverse)
  • Real world, professional experience in a field that utilizes modern day data science tools and methodologies
  • An understanding of the importance of good practices for producing reliable software and reproducible analyses: software project management and collaborative coding, Git and Github, issue tracking, literate analysis tools such as Jupyter and R Markdown
  • Demonstrated enthusiasm and ability to rapidly assimilate new computational and mathematical ideas and techniques on the job, at a more than superficial level, and apply them successfully

Desirable:

  • PhD in Data Science related fields
  • Experience working on social impact projects and passion for making a social impact
  • Experience with handling larger data and computationally expensive ML tasks on clusters or cloud computing environment
  • Experience in using large, scalable relational databases, ranging from PostgreSQL to redshift
  • User interface design and development with web technologies, especially for data visualisation and knowledge representation
  • Developing and/or delivering teaching and training in computational or mathematical methods for research

Benefits

The university is an equal opportunity employer. LMU Munich is interested in increasing the number of female faculty members and strongly encourages women to apply.

Due to the nature of the funding, preference will be given to applicants from EU members states.

Also possible in a part-time capacity.

People with disabilities who are equally as qualified as other applicants will receive preferential treatment.

Contact

How to apply:

  • a short statement letter promoting you as the ideal candidate for the position (~1 page)
  • a detailed CV, with special focus on: obtained degrees, taken classes in relevant topics, publications, programming skills, experience working on social impact projects, track record

Interested applicants should send thenecessary documents in a single PDF document quoting “Technical Mentor Application, DSSGx” via email to:
Juliane.Lauks@stat.uni-muenchen.de

Juliane Lauks, PhD
Scientific Manager
Chair of Statistical Learning & Data Science
Department of Statistics
LMU Munich
Ludwigstr. 33
D-80539 Munich
Germany
https://www.slds.stat.uni-muenchen.de/

Please note
since the Data Science for Social Good (DSSG) Summer Projects are delivered by LMU in Munich in collaboration with the University of Warwick under the DSSGx UK chapter of the DSSG Foundation, your data will be forwarded to members of the DSSG-x executive committee, Challenge Owners and selected LMU/Warwick University/Carnegie Mellon University researchers for the purpose of application review and selection.

Where knowledge is everything. Become part of LMU Munich!

LMU researchers work at the highest level on the great questions affecting people, society, culture, the environment and technology — supported by experts in administration, IT and tech.

LMU offers state-of-the-art research infrastructure, an outstanding international network, attractive career opportunities and a broad spectrum of continuing personal development programs. In doing so, LMU supports its members in nurturing their talents and helping to shape their working environment.

In the course of your application for an open position at Ludwig-Maximilians-Universität (LMU) München, you will be required to submit personal information. Please be sure to refer to our data protection guidelines — in accordance with Article 13 of the General Data Protection Regulation (GDRP) which concerns the collection and processing of personal data — as well as our LMU Privacy Policy: Website. By submitting your application, you confirm that you have read and understood our data protection guidelines and privacy policy and that you agree to your data being processed in accordance with the selection process.