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Middle Data Science Engineer

Data Science
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About AltexSoft

In this role, you will apply statistical modeling and machine learning techniques to complex medical datasets, design experiments, and translate clinical questions into data-driven solutions. You will also work closely with engineers and domain specialists to validate findings, refine predictive logic, and help transition models into production-ready components. This position is a great fit for someone who enjoys applied research, has experience with healthcare data, and wants to contribute to meaningful outcomes in the medical field.

About Project

We are expanding our data science team with a Middle Data Scientist to support a new initiative in the Ophthalmology domain. The goal of the project is to build a predictive engine capable of analyzing patient histories, clinical biomarkers, and treatment patterns to estimate recovery trajectories for various eye-related conditions.

You Have

  • 3+ years of experience as a Data Scientist with a focus on applied ML, predictive modeling, or biostatistics.
  • Core Tech Stack: Strong command of Python and the data science ecosystem (Pandas, NumPy, SciPy, scikit-learn, XGBoost, LightGBM).
  • Modeling Expertise: Expert-level knowledge of supervised learning algorithms (Gradient Boosting, Random Forest, Linear Models) for predicting clinical outcomes (Classification or Regression).
  • Medical Data Handling: Experience handling imbalanced datasets and using appropriate evaluation metrics beyond accuracy (e.g., ROC-AUC, F1-Score, Precision-Recall, Sensitivity/Specificity).
  • Data Engineering: Proficiency in cleaning "noisy" clinical data, feature engineering from patient records, and handling missing values in a medically valid way.
  • Feature Engineering & Selection: Experience preparing datasets: cleaning, feature engineering, and applying rigorous feature selection techniques (Lasso, RFE, Tree-based importance) to identify key clinical biomarkers and prevent overfitting on small cohorts.
  • Database: Familiarity with SQL and working with relational databases for querying patient cohorts.
  • Deployment & MLOps: Experience wrapping models into APIs (Flask/FastAPI) and containerizing applications using Docker for reproducible deployment.
  • Tools: Experience using AI assistants (e.g., Cursor, GitHub Copilot) to accelerate coding workflows.

Would be a plus

  • Domain Knowledge: Background in Health/Medical domain data (Electronic Health Records, Clinical Trial data). ==>* Computer Vision: If the dataset includes medical imaging (OCT scans, Fundus photography), knowledge of Deep Learning (PyTorch/TensorFlow) and CNNs/Vision Transformers is a massive plus.
  • Explainable AI (XAI): Experience specifically in making "Black Box" models interpretable for non-technical medical audiences. - Causal Inference: Knowledge of causal techniques (e.g., DoWhy, CausalML) to distinguish between correlation and actual treatment effects.
  • Cloud ML: Hands-on experience with cloud solutions (Azure ML is preferred for many enterprise health clients, or AWS/GCP).

You Are Going To

  • Model Development: Build and refine machine learning models to predict treatment efficacy and patient recovery probabilities.
  • Feature Extraction: Work with clinical data to extract meaningful predictors (biomarkers, demographic factors, treatment duration) that correlate with eye disease recovery. 
  • Validation: Perform rigorous model validation and error analysis to ensure models are safe and reliable for clinical decision support.
  • Optimization & Simplification: Identify the minimal set of input features required for accurate prediction to ensure the model is clinically actionable and cost-effective to deploy.
  • Explainability: Implement techniques (e.g., SHAP, LIME) to explain why the model predicts a specific outcome, ensuring results are interpretable for doctors/stakeholders.
  • Collaboration: Work closely with product teams and potentially medical domain experts to translate clinical questions into testable analytical approaches.
  • Pipeline Management: Prepare data pipelines together with engineers to support model deployment in a compliant production environment.
  • Model Monitoring: Implement monitoring solutions (e.g., MLflow, Evidently AI, or Prometheus) to track model performance in production and detect data drift or concept drift to ensure continued clinical safety.

We offer

Cup

Work-life Balance

  • Possibility to work remotely
Health

Health Care

  • Reimbursement of medical expenses
  • Online morning exercise
book

Education

  • Compensation for trainings, seminars, conferences
  • Free access to the Pluralsight and ACloudGuru knowledge base
  • Access to the AltexSoft library with  top-notch materials
  • A mentor for a probation period
  • Engagement in our Mentorship Hub program as a mentor or a mentee to foster professional growth and development 
gora

Development

  • Horizontally — master new technologies at internal courses
  • Vertically — choose your own career path through Competency trees
medal

Recognition Program

  • All your activities are marked by points that can be exchanged for gifts to fit any taste.
Contact our Talent Acquisition Specialist
Viktoriia Vorobiovaviktoriia.vorobiova@altexsoft.com

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Come along if you share our vision

  • We were founded in 2007. Employer of the Year (2014, 2017, 2019).
  • The AI Ukraine conference and the Know Your Onions meetups organizer.
  • R&D centers in Ukraine (Kharkiv, Kremenchuk, Lviv) and Georgia (Tbilisi). We employ more than 300 people.