Machine Learning Scientist

Bravofly; Chiasso; Vor Ort; CHF 80’000 - 120’000; Vollzeit

Zusammenfassung

Join a dynamic team as a Machine Learning Scientist at a leading European Travel-Tech company. This innovative firm is dedicated to enhancing travelers' experiences through cutting-edge machine learning solutions. You will be at the forefront of developing real-time algorithms and collaborating with a diverse team of experts from various fields. With a focus on creativity and problem-solving, this role offers the chance to tackle real-world challenges using big data. Embrace a flexible working environment, enjoy a shorter workweek, and contribute to a culture that values diversity and inclusion. If you are passionate about technology and eager to make a difference, this opportunity is perfect for you.

Leistungen

Shorter working week

Flexible working hours

Paid volunteering days

Travel industry discounts

Professional development training

Social events

Support for significant life events

Qualifikationen

• Ph.D. or Master’s with 3 years in machine learning required.
• Strong knowledge of supervised and unsupervised methods.
• Experience with deep learning frameworks essential.

Aufgaben

• Develop real-time machine learning solutions for big data challenges.
• Create ranking algorithms using deep learning techniques.
• Collaborate with cross-functional teams on machine learning projects.

Kenntnisse

Supervised methods

Unsupervised methods

Python

SQL

Deep Learning

Creative problem-solving

Collaboration

Ausbildung

Ph.D. in Computer Science, Mathematics, or Physics

Master’s degree with 3 years of experience

Tools

scikit-learn

Pandas

matplotlib

Numpy

Scipy

Keras

TensorFlow

PyTorch

Jobbeschreibung

The Company

At lastminute.com, we live for the holidays. We are the European Travel-Tech leader in Dynamic Holiday Packages. With technology, we turn spontaneous thoughts into meaningful experiences, helping people travel the world.

We are looking for a Machine Learning Scientist to join our team of around 1,700 people worldwide to help us power up the travelers’ journey for millions of holidaymakers. If you are a motivated machine learning scientist who loves to work with challenging real-world problems on big data, keep reading, as you might be the perfect fit for this job.

Job Description

Job Title: Machine Learning Scientist
Working model: hybrid at Chiasso office
Team: Strategic Analytics team in the Technology department.
Level: Professional
Location: Chiasso, Switzerland
Contract: Permanent – full-time (36 h/week)

What your impact will be:

• Develop real-time machine learning solutions to deal with challenging real-world problems on big data.
• Develop Ranking Algorithms based on deep learning.
• Collaborate in a cross-functional team, including machine learning scientists, software engineers, machine learning engineers, and project managers.

Qualifications

Your expertise:

• A Ph.D. in Computer Science, Mathematics, or Physics, or a Master’s degree with a minimum of 3 years of experience in machine learning.
• Excellent knowledge of Supervised methods (Classification, Regression) and Unsupervised methods (Clustering, Feature Selection, Dimensionality Reduction).
• Good knowledge of Python and SQL, with knowledge of important Python libraries for Machine Learning and Data Analysis (scikit-learn, Pandas, matplotlib, Numpy, Scipy, MLflow).
• Experience with Deep Learning (Recurrent Neural Networks, Convolutional Neural Networks, and Autoencoders).
• Experience with Keras (and TensorFlow) or PyTorch.
• Experience in Uncertainty Estimation (Monte Carlo Dropout, Deep Ensembles).
• Able to find creative solutions to interesting problems.
• Curious with a constant desire to learn and collaborate.

Additional Information

Perks of working with us:

How we work together:
– An inclusive, friendly, and international environment (you’ll be working with colleagues from +10 countries and over 48 nationalities).
– Shorter working week, with a half working day on Fridays.
– Flexible start and end of the working day.
– Possibility to work from anywhere for a period of time per year defined according to local regulations.

How we learn together:
– Fri-Yays: half a day on Friday morning with a no-meeting mandate and dedicated to deep work, personal growth, learning and training and/or focus time.
– Professional and managerial skills development training paths, access to e-learning platforms such as O’Reilly, Udemy, Coursera (depending on the department), and to our internal platform offering bespoke training content.

Other perks:
– 2 paid days off per year for volunteering purposes.
– Occasional social events to foster connections among colleagues.
– Travel industry discounts and flash exclusive staff fares.
– We support our employees through life’s significant moments with leave options (e.g parental responsibilities, marriages, bereavements, relocations, etc.) in line with local laws.

Selection process steps:
– HR interview (30 minutes).
– 1st interview (Manager 1 hour): Machine learning + Python coding.
– 2nd interview (Director 45 min): Soft skills + machine learning knowledge.
– Offer extended.
(*Please note the process can slightly vary. The recruiter in charge will share more details when setting up the interview.)

Our commitment to celebrate diversity and generate belonging:
At the heart of our culture is a commitment to inclusion across race, gender, age, sexual orientation, religion, gender identity or expression, and accessibility. We strongly believe in an equal opportunity space, which is welcoming and celebrates the uniqueness of everyone who works here. We value different lived experiences and respect viewpoints, as we know unicity drives innovation. We want to make sure our people reflect the communities across the world we help travel.

Eligibility criteria:
By submitting your information and application, you confirm that you are legally authorised to work in the country of employment and that you do not require visa sponsorship to obtain employment visa status.

Please submit your resume in English.