Data Scientist – Machine Learning

Client Scientific
Location Kraków, Poland
Region Kraków
Offer description

Selvita is a global integrated service provider with laboratories in Poland (Krakow, Poznan, and Wroclaw), Croatia (Zagreb), and offices in Cambridge, UK, as well as the Greater Boston Area and San Francisco Bay Area in the US. Our company is dedicated to delivering comprehensive solutions that support our international clients in drug discovery and drug development. As one of the largest preclinical contract research organizations in Europe, Selvita is driven by a clear mission: to bridge the gap between early drug discovery and the clinical stage of drug development. 

At Selvita, we value partnership, excellence, passion, commitment, and integrity. We build strong, collaborative relationships with our clients and commit to the highest standards of quality. We fuel our pursuit of innovative solutions with enthusiasm, advance drug development with focused dedication, and ensure honesty and transparency in all our work.

Currently we are seeking a Data Scientist – Machine Learning who will be responsible for developing, training, and optimizing machine learning models, curating datasets, conducting data analysis, and collaborating with clients and cross-functional teams to deliver AI-driven solutions for complex problems in drug discovery.

Join us at Selvita, where these values guide our mission to advance drug discovery to the clinical stage of drug development.  

Key Responsibilities
  • Developing, training and optimizing of Machine Learning models  
  • Creating and curating relevant datasets needed for model training 
  • Perform exploratory data analysis to understand its features and patterns 
  • Follow a rigorous experimentation strategy for machine learning models, including defining clear evaluation metrics to extract meaningful insights from the results 
  • Documenting model development, experiments, and results in a clear and reproducible manner 
  • Translating business requirements to Machine Learning solutions 
  • Working on complex and barely explored problems within the field of Drug Discovery 
  • Collaborating with clients to ensure a shared understanding of project goals, managing clients’ expectations and taking ownership of the project structure and outcome 
  • Engaging in cross-functional collaborations to leverage AI to increase productivity 
  • Staying up to date in the field of machine learning and AI 

 

Please note that this is an onsite position and will be based at our Cracow office.

Your Background
  • Excellent understanding of Machine Learning concepts (including: deep learning, classical ML, probability theory) 
  • Preferably experience working with graph neural networks or transformer architectures 
  • Experience working with full life cycle of ML model creation and deployment (including: dataset creation, model training, hyper-tuning, assessment of model performance) 
  • Proficiency using python and relevant ML libraries (torch, scikit-learn, numpy, pandas, ect.) 
  • Ability to write clean, modular, and maintainable code 
  • Understanding of algorithms and data structures 
  • Proficiency in using Linux operating system 
  • Ability to communicate complex ML topics to non-technical people 
  • Interest in chemistry and biology, and the field of Drug Discovery 


We are considering candidates for both junior and senior positions, according to the level of experience. 

Your Benefits Package
  • Working in world-class extensive research facilities and modern laboratories
  • Daily cooperation and know-how exchange with scientific experts
  • Additional benefits: a prepaid lunch card, private medical care, subsidized sports card, and office fruit provision
  • Internal Development initiatives including soft & leadership skills training programs
  • Recognition Program
  • Employee Referral Program
  • English & Polish language courses
  • Support & incentive bonus for completing Ph.D
  • Support in the legalization process and relocation package
  • Various sports and engagement initiatives
Last modified Monday, October 14, 2024