Upstaff Sign up
Jorge D.
🇩🇰Denmark
Created AtUpstaffer since May, 2024

Jorge D. — Data Scientist, Biomedical Engineer

Expertise in Data Science.

Last verified on May, 2024

Core Skills

Bio Summary

- 2+ years of experience as a Data Scientist at QIAGEN (biotech) and at DELOITTE (consulting)
- 2 years experience as Data Engineer & ML Researcher at CEREBRIU (AI & Medical Imaging)
- BSc in Biomedical Engineering, MSc in Data Science

Technical Skills

Programming LanguagesPython, R
AI & Machine LearningComputer Vision, LLM, Machine Learning, NLP, NumPy, OpenCV, PyTorch, Scikit-learn, TensorFlow, Transformer models
Python Libraries and ToolsNumPy, Pandas, PySpark, PyTorch, Scikit-learn, SciPy, TensorFlow
R FrameworksShiny
R Libraries and Toolstidyverse
Data Analysis and Visualization TechnologiesETL, Pandas, Power BI
Databases & Management Systems / ORMSQL
Amazon Web ServicesAWS Boto3, AWS S3
Soft SkillsAnalytic Skills
Deployment, CI/CD & AdministrationCI/CD
Virtualization, Containers and OrchestrationDocker, Kubernetes
Version ControlGit
Operating SystemsLinux
Other Technical SkillsMedical Imaging, MLOps, nibabel, nilearn, pyarrow, skimage, XNAT RedBrick

Experience 

MACHINE LEARNING RESEARCHER, CEREBRIU (AI & radiomics)

Sep 2023 - Present

Within the ML team and in collaboration with academia for my MSc thesis, I focus on automating the segmentation & detection of Cerebral Microbleeds in MRI brain data through Deep Learning.

  • Continuous Literature research
  • Design and coordinate the creation of a new clinically relevant dataset of microbleeds in collaboration with radiologists.
  • Refinement and preprocessing of public and private datasets Training of 3D segmentation and detection models
  • Investigating: multi-task and transfer learning, augmentations 37.5 ECTS. Preliminary results: https://shorturl.at/bHLSW

 

DATA ENGINEER, CEREBRIU (AI & radiomics)

Nov 2022 - Present

In the Data Management & Analytics team, my efforts are directed towards on processing and managing data to facilitate deep learning model training within the Research department.

  • Managing internal database of MRI images, annotation maps, and medical reports on Amazon S3 and XNAT
  • Improving the processing pipeline for ingesting new hospital data 
  • Deploying Large Language Models for automatic labeling, classification, NER, and anonymization of medical reports
  • Leading technical aspects of multiple data annotation projects through data analysis, image processing, and Sdk interactions
  • Coordinating closely with medical annotators and radiologists

 

DATA ANALYST, Deloitte Digital (consulting)

Sep 2021 - Aug 2022

Technical lead for various marketing automation and digitalization projects across automotive, banking, and airline industries.

  • Developing automation scripts for ETL pipelines, analytics, KPI calculations, and SFMC campaigns launching & Surveillance
  • Predictive modeling for customer behavior Data reporting to clients with real-time dashboards & reports

 

JUNIOR DATA SCIENTIST, QIAGEN (biotech)

Nov 2019 - Sep 2021

Within the Data Science team, handling qPCR & production data.

  • Developed custom R-Shiny apps and deployed on Linux servers R Backend coding (R package, API interactions, processing)
  • Instituted a Data Catalog and coding standards for the team
  • Administering Linux servers for computation & data hosting
  • Producing statistical data analysis for various departments
  • Tuning a classification algorithm for new DiagCORE panels

Prior to the Data team, I worked for 9 months in the R&D department for production as a Process Engineer. Scripting and doing wet lab tasks.

 

BSC THESIS - COMPUTATIONAL NEUROSCIENCE, IDIBAPS (research)

Feb 2020 - Jul 2020

Within Systems Neuroscience research group. The goal was to investigate using long-temporal-scale information by various Recurrent Neural Networks to understand decisionmaking biases observed in real experiments with mice.

Contributed to NeuroGym open-source package.

  • Article preprint: https://osf.io/preprints/psyarxiv/aqc9n
  • Thesis report: https://shorturl.at/mITW0

Education

MASTER OF SCIENCE - DATA SCIENCE

IT University of Copenhagen | Sep 2022 - Jun 2024 (120 ECTS)

Grade: 10.2 (average), 12 (mode), 7-step scale

Theoretical and applied foundations of:

  • Algorithm Design & Programming
  • Advanced Statistics & Calculus
  • Advanced ML & NLP (RNNs, CNNs, Transformers, GANs, VAEs)
  • Production DS (Data wrangling, Databases, DevOps)
  • Computer Systems Performance
  • Medical Image Analysis & Processing

Projects can be found here: https://shorturl.at/xIL37.

 

BACHELOR OF SCIENCE - BIOMEDICAL ENGINEERING

University of Barcelona | Sep 2016 - Jul 2020 (240 ECTS)

Grade: 8.7 (average), 9 (mode), 0-10 point scale 11 Distinctions with Honours

ENGINEERING knowledge: Algebra · Calculus · Differential Equations · Physics · Biophysics · Computer Science · Statistics · Programming · Electronics · Signal Processing · Computational Modeling · [Bio]Materials · Robotics · Bioinformatics · ML BIOMEDICAL knowledge: Cell & Molecular Biology · Biotechnology [Bio]Physics · [Bio]Chemistry · Pharmacology · Human Anatomy & Physiology · Tissue Engineering · Medical Instrumentation · Clinical Engineering · Nanotechnology · Medical Imaging

 

EXCHANGE SEMESTER - BIOMEDICAL ENGINEERING

Delft University of Technology (Netherlands) | Jan - Aug 2019

Taking 60 ECTS of courses from MSc in Biomedical Engineering

How to hire with Upstaff

1

Talk to Our Talent Expert

Our journey starts with a 30-min discovery call to explore your project challenges, technical needs and team diversity.

2

Meet Carefully Matched Talents

Within 1-3 days, we’ll share profiles and connect you with the right talents for your project. Schedule a call to meet engineers in person.

3

Validate Your Choice

Bring new talent on board with a trial period to confirm you hire the right one. There are no termination fees or hidden costs.

Why Upstaff

Upstaff is a technology partner with expertise in AI, Web3, Software, and Data. We help businesses gain competitive edge by optimizing existing systems and utilizing modern technology to fuel business growth.

Real-time project team launch

<24h

Interview First Engineers

Upstaff's network enables clients to access specialists within hours & days, streamlining the hiring process to 24-48 hours, start ASAP.

x10

Faster Talent Acquisition

Upstaff's network & platform enables clients to scale up and down blazing fast. Every hire typically is 10x faster comparing to regular recruitement workflow.

Vetted and Trusted Engineers

100%

Security And Vetting-First

AI tools and expert human reviewers in the vetting process is combined with track record & historically collected feedbacks from clients and teammates.

~50h

Save Time For Deep Vetting

In average, we save over 50 hours of client team to interview candidates for each job position. We are fueled by a passion for tech expertise, drawn from our deep understanding of the industry.

Flexible Engagement Models

Arrow

Custom Engagement Models

Flexible staffing solutions, accommodating both short-term projects and longer-term engagements, full-time & part-time

Sharing

Unique Talent Ecosystem

Candidate Staffing Platform stores data about past and present candidates, enables fast work and scalability, providing clients with valuable insights into their talent pipeline.

Transparent

$0

No Hidden Costs

Price quoted is the total price to you. No hidden or unexpected cost for for candidate placement.

x1

One Consolidated Invoice

No matter how many engineers you employ, there is only one monthly consolidated invoice.

Ready to hire Jorge D.
or someone with similar Skills?
Looking for Someone Else? Join Upstaff access to All profiles and Individual Match
Start Hiring