Jobs Data Science

We are looking for an experienced Data Scientist for a full-time job to join our team.


Requirements:
- 2+ years of experience as a data scientist
- BSc in Mathematics, Statistics, Computer Science, Economics, or another related field.
- Experience in using Python & SQL
- Experience with Airflow and GCP
- Experience with Git & CI/CD
- Upper Intermediate English — written and spoken
- Ability to design creative solutions for complex requirements
- Ability to learn and lead projects independently, and to work with minimal supervision with customers (tech & business)

Responsibilities:
- Conduct independent research, including defining research problems, creating research plans, designing experiments, developing algorithms, implementing code, and performing comprehensive comparisons against existing benchmarks;
- Clearly communicate your research findings to both technical and non-technical audiences
- Work on various data sources and apply sophisticated feature engineering capabilities
- Bring and use business knowledge
- Build and manage technical relationships with customers and partners.

We offer:
- remote time job, B2B contract
- 12 sick leaves and 18 paid vacation business days per year
- Comfortable work conditions (including MacBook Pro and Dell monitor on each workplace)
- Smart environment
- Interesting projects from renowned clients
- Flexible work schedule
- Competitive salary according to the qualifications
- Guaranteed full workload during the term of the contract
 

We are looking for a Computer Vision Engineer with a solid foundation in classical computer vision and hands-on experience implementing low-level CV algorithms. The ideal candidate will have exposure to SLAM, Visual-Inertial Odometry (VIO), and sensor fusion, with responsibilities adjusted according to experience level.

Responsibilities: 

• Design, implement, and optimize classical CV algorithms independently
• Develop and maintain components of SLAM/VIO pipelines
• Contribute to sensor fusion modules involving visual and inertial data
• Lead experiments, evaluate model performance, and propose improvements
• Review research papers and translate findings into practical solutions
• Collaborate across teams to ensure smooth integration of CV components.

Required Qualifications:

• Strong practical experience with classical CV and geometric vision
• Hands-on experience developing components of SLAM and/or VIO systems
• Proficiency in C++ and confident working in Linux environments
• Solid understanding of multi-sensor systems and data fusion
• Proven ability to prototype, benchmark, and optimize CV algorithms
• Ability to analyze research papers and implement state-of-the-art methods.

Nice to Have:

• Experience with Python
• Knowledge of neural networks and CV libraries/frameworks (OpenCV, NumPy, PyTorch, ONNX, Eigen, etc.)
• Experience with sensor fusion techniques
• Familiarity with embedded or real-time systems

We are looking for a Computer Vision Engineer with a solid foundation in classical computer vision and hands-on experience implementing low-level CV algorithms. The ideal candidate will have exposure to SLAM, Visual-Inertial Odometry (VIO), and sensor fusion, with responsibilities adjusted according to experience level.

Responsibilities: 

• Implement and optimize low-level computer vision algorithms under guidance
• Contribute to modules related to feature extraction, tracking, matching, and geometric processing
• Assist in the integration and testing of SLAM/VIO components
• Perform experiments, analyze results, and help prepare reports
• Work closely with senior engineers to explore and apply recent CV research.

Required Qualifications:

• 1+ years of hands-on experience with classical computer vision
• Basic understanding of geometric computer vision principles
• Experience implementing low-level CV algorithms (e.g., filtering, feature detection, camera models)
• Familiarity with SLAM or VIO concepts
• Working knowledge of C++ and Linux
• Ability to learn quickly and navigate recent CV researc
• Degree or ongoing studies in a STEM field.

Nice to Have:

• Experience with Python
• Knowledge of neural networks and CV libraries/frameworks (OpenCV, NumPy, PyTorch, ONNX, Eigen, etc.)
• Experience with sensor fusion techniques
• Familiarity with embedded or real-time systems

About the Company

 European deep-tech company developing innovative AI solutions at the intersection of sensor technology, data science, and machine learning. Our products transform complex data from physical sensors into accurate analytical insights for real-time decision-making across multiple industries, including environmental monitoring, product quality control, and applied research.

The team works at the intersection of artificial intelligence, signal processing, and experimental analytics, building advanced models for pattern recognition and predictive analysis based on complex datasets.

Responsibilities

-Process, clean, and validate data for building ML/DL models.

-Design, implement, and optimize Machine Learning and Deep earning models.

-Conduct experiments including training, testing, fine-tuning, and hyperparameter optimization.

-Analyze results and present insights clearly to the team.

-Improve data collection and preprocessing pipelines.

-Collaborate closely with the ML Lead and engineering team.

-Participate in selecting modeling approaches and experimental strategies.

-Work with sensor-generated and multidimensional datasets.

Requirements

-5+ years of experience in Data Science / Machine Learning / Deep Learning.

-Bachelor’s or Master’s degree in Computer Science, Engineering, Applied Mathematics, or a related field.

-Commercial experience building and deploying ML/DL models.

Strong understanding of algorithms:

-Classification and regression

-Clustering

-CNN and fully connected neural networks

-Time-series analysis

-Experience working with databases (SQL or solutions from Oracle Corporation).

Practical experience with:

-Python

-PyTorch or TensorFlow

-scikit-learn

-NumPy, Pandas

-Experience training models in cloud environments (e.g., Amazon Web Services).

-Experience working in a Linux environment.

-Experience using Git.

-English level sufficient for technical communication.

Nice to Have

-Experience working with sensor or experimental datasets.

-Understanding of signal processing or pattern recognition.

-Experience building experimental ML pipelines. 

Requirements:

• 5+ experience in applying data science and machine learning techniques
• Python programming using classic scientific stack (numpy, pandas, scipy, scikit-learn, pytorch, lightgbm, matplotlib or any other visualization library)
• Understanding of classic machine learning algorithms and theory (supervised/unsupervised learning techniques, metrics and visualizations for interpretation of model performance, time-series analysis, anomaly detections, recommendation models, hyperparameter optimization)
• NLP (NLP techniques, GenAI, LLM finetuning)
• Statistics and optimization basic understanding
• Data manipulation and cleaning skills (fill missing entries / filter dataframes / apply certain changes to columns)
• Prior experience with Python web frameworks would be great, but not critical (FastAPI, Flask or anything else)
• Dockerization technologies
• Cloud solutions (any of AWS, GCP, Azure)
• Understanding of prompt engineering or experience with building scripts or applications around LLMs
• Proficient with SQL (Postgres, Snowflake), NoSQL (ElasticSearch, Mongo(optional))
• Monitoring services
   
• Responsibilities:
  Work on time series forecasting problems with real, complex data. Implement features, interpret their importance, train models and incorporate them in the production pipeline.
• Work on LLM-based solutions, which use techniques like Contextual RAG (Retrieval Augmented Generation), LLM as Judge, semantic search, chatbot services, etc.
• Create anomaly detection models and clustering methods for non-stationary, “dirty”, semi-organized data

Data Science UA is a service company with strong data science and AI expertise. Our journey began in 2016 with the organization of the first Data Science UA conference, setting the foundation for our growth. Over the past 9 years, we have diligently fostered one of the largest Data Science & AI communities in Europe.

About the role:
We are looking for a Computer Vision / Machine Learning Engineer to develop offline CV models for industrial visual inspection.

Your main task will be to design, train, and evaluate models on inspection data in order to:

• Improve discrimination between good vs. defect samples
• Provide insights into key defect categories (e.g., terminal electrode irregularities, surface chipping)
• Significantly reduce false-positive rates, optimizing for either precision, or recall
• Prepare the solution for future deployment, scaling, and maintenance
•  

Key Responsibilities:
Data Analysis & Preparation
- Conduct dataset audits, including class balance checks and sample quality reviews
- Identify low-frequency defect classes and outliers
- Design and implement augmentation strategies for rare defects and edge cases
Model Development & Evaluation
- Train deep-learning models on inspection images for defect detection
- Use modern computer vision / deep learning frameworks (e.g., PyTorch, TensorFlow)
- Evaluate models using confusion matrices, ROC curves, precision–recall curves, F1 scores and other relevant metrics
- Analyze false positives/false negatives and propose thresholds or model improvements
Reporting & Communication
- Prepare clear offline performance reports and model evaluation summaries
- Explain classifier decisions, limitations, and reliability in simple, non-technical language when needed
- Provide recommendations for scalable deployment in later phases (e.g., edge / on-prem inference, integration patterns)

Candidate Requirements:
Must-have:
- 1-2 years of hands-on experience with computer vision and deep learning (classification, detection, or segmentation)
- Strong proficiency in Python and at least one major DL framework (PyTorch or TensorFlow/Keras)
- Solid understanding of:

• Image preprocessing and augmentation techniques
• Classification metrics: accuracy, precision, recall, F1, confusion matrix, ROC, PR curves
• Handling imbalanced datasets and low-frequency classes

- Experience training and evaluating offline models on real production or near-production datasets
- Ability to structure and document experiments, compare baselines, and justify design decisions
- Strong analytical and problem-solving skills; attention to detail in data quality and labelling
- Good communication skills in English (written and spoken) to interact with internal and client stakeholders

Nice-to-have:
- Experience with industrial / manufacturing computer vision (AOI, quality inspection, defect detection, etc.)
- Familiarity with ML Ops/deployment concepts (ONNX, TensorRT, Docker, REST APIs, edge devices)
- Experience working with time-critical or high-throughput inspection systems
- Background in electronics, semiconductors, or similar domains is an advantage
- Experience preparing client-facing reports and presenting technical results to non-ML audiences

We offer:
- Free English classes with a native speaker and external courses compensation;
- PE support by professional accountants;
- 40 days of PTO;
- Medical insurance;
- Team-building events, conferences, meetups, and other activities;
- There are many other benefits you’ll find out at the interview.

We’re opening applications for a 3-month intensive internship where participants will work on real company projects under the guidance of Senior Data Scientists with years of hands-on experience.

PARTICIPANTS

We’re looking for motivated and ambitious individuals eager to sharpen their Data Science skills.

To participate in the internship program, you need:

- Strong Python knowledge;
- Good skills in programming and algorithms;
- Good background in Math;
- Hand on experience in Machine Learning/Computer Vision/NLP (including pet projects);
- Knowledge of base ML libraries in Python;
- Strong knowledge of Git;
- English level Intermediate (B2).

STRUCTURE / MENTOR

- Kick-off Session — meet your mentor, fellow interns, and dive into the Skill Evaluation stage.
- Project-Based Learning — solve real Data Science tasks, collaborate on projects, and gain experience in a structured learning environment.
- Continuous Mentorship — benefit from ongoing guidance, code reviews, and feedback sessions designed to accelerate your growth.
- Every intern will work under the guidance of Senior Data Scientists with 5+ years of industry experience.

RESULTS

- Practical experience in AI, Machine Learning, Deep Learning, NLP, and Computer Vision.
- The chance to work on impactful, production-level projects.
- Opportunity to receive a full-time job offer at WINSTARS.AI upon successful completion of the internship.

About us
 

Fozzy Group is one of the largest trade industrial groups in Ukraine and one of the leading Ukrainian retailers,

with over 700 outlets all around the country. It is also engaged in e-commerce, food processing & production,

agricultural business, parcel delivery, logistics and banking. 

Since its inception in 1997, Fozzy Group has focused on making innovative business improvements, creating

new opportunities for the market and further developing the industry as a whole.
 

Job Description:
 

The Foodtech team is looking for a Data Scientist to develop the Operational Analytics function for a fast[1]growing food delivery business. In this role, you will focus on time series forecasting, regression modeling,

simulation modeling, and end-to-end machine learning pipelines to support resource planning and

operational decision-making.

You will be responsible for developing simulation-based models that serve as a foundation for a digital twin

of operational processes, enabling scenario analysis, stress testing, and what-if simulations for capacity

planning and operational optimization.

You will work closely with product, engineering, and operations teams to transform data into measurable

business impact through production-ready ML and simulation solutions.
 

Job Responsibilities
 

• Develop and implement time series forecasting models for resource planning (demand, capacity,

couriers, delivery slots, operational load);

• Build regression and machine learning models to explain key drivers and support operational

decisions;

• Apply a wide range of time series approaches from classical models (SARIMA, ETS, Prophet) and

ML models (GB) to modern Deep Learning models (LSTM, Temporal CNNs, Transformers for TS);

• Design, build, and maintain end-to-end automated ML pipelines, deploy and operate models in

production using AWS SageMaker;

• Orchestrate training and inference workflows with Apache Airflow;

• Analyze large-scale operational datasets and convert results into insights, forecasts, and actionable

recommendations;

• Collaborate with product managers, engineers, and operations teams to define business problems

and validate analytical solutions;

• Monitor model performance, forecast stability, and business impact over time.
 

Requirements
 

• Bachelor’s Degree in Mathematics / Engineering / Computer Sciences / Quantitative Economics /

Econometrics;

• Strong mathematical background in Linear algebra, Probability, Statistics & Optimization Techniques;

• At least 2 years working experience on Data Science;

• Experience of the full cycle of model implementation (data collection, model training and evaluation,

model deployment and monitoring);

• Ability to work independently, proactively, and to decompose complex problems into actionable tasks.
 

Skills

Must Have
 

• Strong proficiency in Python with solid application of object-oriented programming (OOP) principles

(modular design, reusable components, maintainable code);

• Solid experience in time series forecasting and regression modeling;

• Practical knowledge of:

o Classical and ML forecasting techniques;

o Statistical methods (hypothesis testing, confidence intervals, A/B testing);

• Advanced SQL skills (window functions, complex queries);

• Experience building automated ML pipelines;

• Understanding of MLOps principles (model versioning, monitoring, CI/CD for ML).
 

Preferred
 

• Hands-on experience with AWS SageMaker (training jobs, endpoints, model registry);

• Experience with Apache Airflow for data and ML workflow orchestration;

• Knowledge of Reporting and Business Intelligence Software (Power BI, Tableau);

• Experience working with large-scale production data systems.
 

What We Offer
 

• Competitive salary;

• Professional & personal development opportunities;

• Being part of dynamic team of young & ambitious professionals;

• Corporate discounts for sport clubs and language courses;

• Medical insurance package

Hello!

We are E-Com, a team of Foodtech and Ukrainian product lovers.

And we also break stereotypes that retail is only about tomatoes. Believe me, the technical part of our projects provides a whole field for creativity and brainstorming.

What we are currently working on:

• we are upgrading the existing delivery of a wide range of products from Silpo stores;
• we are developing super-fast delivery of products and dishes under the new LOKO brand.

We are developing a next-generation Decision Support Platform that connects demand  planning, operational orchestration, and in-store execution optimization into one unified Analytics and  Machine Learning Ecosystem. 

The project focuses on three major streams: 

•  Demand & Forecasting Intelligence: building short-term demand forecasting models, generating  granular demand signals for operational planning, identifying anomalies, and supporting commercial  decision logic across virtual warehouse clusters. 

•  Operational Orchestration & Task Optimization: designing predictive models for workload  estimation, task duration (ETA), and prioritization. Developing algorithms that automatically map  operational needs into structured tasks and optimize their sequencing and allocation across teams. 

•  In-Store Execution & Routing Optimization: developing models that optimize picker movement,  predict in-store congestion, and recommend optimal routes and execution flows. Integrating store  layout geometry, product characteristics, and operational constraints to enhance dark-store  efficiency. 

You will join a cross-functional team to design and implement data-driven decision module that directly  influence commercial and operational decisions. 

Responsibilities:

•  develop and maintain ML models for forecasting short-term demand signals and detecting anomalies  across virtual warehouse clusters;

•  build predictive models to estimate task workload, execution times (ETA), and expected operational  performance;

•  design algorithms to optimize task distribution, sequencing, and prioritization across operational  teams;

•  develop routing and path-optimization models to improve picker movement efficiency within dark  stores; 

•  construct data-driven decision modules that integrate commercial rules, operational constraints, and  geometric layouts;

•  translate business requirements into ML-supported decision flows and automate key parts of  operational logic; 

•  build SQL pipelines and data transformations for commercial, operations, and logistics datasets;

•  work closely with supply chain, dark store operations, category management, and IT to deliver  measurable improvements;

•  conduct A/B testing, validate model impact, and ensure high-quality model monitoring. 

Requirements:

•  bachelor’s Degree in Mathematics / Quantitative Economics / Econometrics / Statistics / Computer  Sciences / Finance; 

•  at least 2 years working experience on Data Science; 

•  strong mathematical background in Linear algebra, Probability, Statistics & Optimization Techniques; 

•  proven experience with SQL (Window functions, CTEs, joins) and Python;

•  expertise in Machine Learning, Time Series Analysis and application of Statistical Concepts  (Hypothesis testing, A/B tests, PCA); 

•  ability to work independently and decompose complex problems. 

Preferred:

•  experience with Airflow, Docker, or Kubernetes for Data Orchestration; 

•  practical experience with Amazon SageMaker: training, deploying, and monitoring ML models in a  production environment; 

•  knowledge of Reporting and Business Intelligence Software (Power BI, Tableau, Looker); 

•  ability to design and deliver packaged analytical/ML solutions. 

What we offer

• competitive salary;
• opportunity to work on flagship projects impacting millions of users;
• flexible remote or office-based work (with backup power and reliable connectivity at SilverBreeze Business Center);
• flexible working schedule;
• medical and Life insurance packages;
• support for GIG contract or private entrepreneurship arrangements;
• discounts at Fozzy Group stores and restaurants;
• psychological support services;
• Caring corporate culture;
• a team where you can implement your ideas, experiment, and feel like you are among friends.

Almus is looking for a Data Scientist to join our Analytics team and build production-grade machine learning models that directly impact marketing and business performance.

You will work on end-to-end ML solutions, from data and features to deployment and monitoring, focusing on improving LTV prediction quality, optimizing ML-driven costs, and driving key metrics such as LTV, ROAS, retention, and CAC. This is an individual contributor role with strong ownership, close collaboration with Marketing, Product, and Data teams, and a clear focus on real business impact.

Apply to join Almus and take ownership of high-impact data initiatives!

Responsibilities

• Design, develop, and deploy machine learning models to production
• Improve product and business decision-making through data-driven approaches
• Build and evolve end-to-end ML pipelines (data → features → model → inference → monitoring)
• Drive measurable impact on key product and commercial metrics
• Standardize ML approaches within the team (best practices, documentation, reproducibility)
• Provide technical input to the architecture of analytics and ML infrastructure
• Develop and deploy models that drive growth in LTV, ROAS, retention, and CAC
• Influence performance and lifecycle marketing strategy
• Act as a domain expert and collaborate closely with Marketing, Product, and Data Engineering teams

What We Look For

• 3+ years of experience as a Data Scientist / ML Engineer
• Experience working with mobile subscription-based products
• Strong Python skills (production-level code)
• Solid knowledge of classical machine learning algorithms and practical experience applying them
• Experience with feature engineering, model evaluation, and bias–variance trade-offs
• Hands-on experience with marketing models such as LTV, churn, cohort, and funnel modeling
• Experience with attribution, incrementality, and uplift modeling
• Strong SQL skills and experience working with analytical datasets
• Experience with production ML systems and A/B testing

• English level: Intermediate+

   

Nice to have

• Experience with BigQuery
• MLOps experience (Docker, CI/CD, model registres)
• Experience working with performance marketing data (Meta, Google Ads, Adjust)
• Knowledge of causal inference

• Experience with AutoML and Bayesian models

   

We Offer

• Exciting challenges and growth prospects together with an international company
• High decision-making speed and diverse projects
• Flexibility in approaches, no processes for the sake of processes
• Effective and friendly communication at any level
• Highly competitive compensation package that recognizes your expertise and experience, Performance Review practice to exchange feedback and discuss terms of cooperation
• Flexible schedule, opportunity to work in a stylish and comfortable office or remotely
• Respect for work-life balance (holidays, sick days - of course)
• Bright corporate events and gifts for employees
• Additional medical insurance
• Compensation for specialized training and conference attendance
• Restaurant lunches at the company's expense for those working in the office, endless supplies of delicious food all year round
   

We are looking for a Senior Data Scientist who can deliver end-to-end solutions, communicate both technical and business aspects to clients, and take ownership of production outcomes — helping Dastellar fulfill its mission to build custom AI/ML/Data Science solutions for businesses.

General Requirements:

• 5+ years in ML/Data Science.
• Strong experience with classical ML: sklearn, XGBoost/LightGBM, time-series models (ARIMA/Prophet/ETS or deep models), and recommender systems.
• Python 3.8+ proficiency; solid Pandas/NumPy; production-level experience with PyTorch or TensorFlow.
• Experience deploying ML models (Docker, CI/CD); basic cloud skills (AWS/GCP/Azure) preferred.
• Ability to communicate technical concepts to clients and translate risks into business impact.
• Autonomous and proactive; history of leading end-to-end tasks.
• English: B2+.
   

Nice-to-have / Growth Opportunities:

• LLM/GenAI experience: fine-tuning GPT/LLaMA/Hugging Face, building RAG/embedding pipelines, LangChain.
• Vector DBs: FAISS, Qdrant, Pinecone.
• MLOps basics: MLflow, W&B, CI/CD pipelines.
• Big Data: Spark, advanced SQL optimization.
• Domain expertise: energy or retail.
   

Main Responsibilities:

• Build classical ML models (classification, regression, clustering, recommendation, time-series).
• Data cleaning, validation, feature engineering, ETL/ELT pipelines.
• Design and deploy ML/GenAI solutions from POC to production.
• Integrate models with infrastructure (Docker, CI/CD; optionally Kubernetes/serverless).
• Monitor model performance and system health (metrics, data drift, alerts).
• Document solutions, create client presentations, and conduct R&D experiments.
• Maintain code quality: tests, reviews, reproducibility.
   

Soft Skills:

• Responsibility & ownership: end-to-end project delivery, meeting deadlines, post-release SLAs.
• Proactivity: proposing and implementing process or architecture improvements.
• Autonomy & problem-solving: translating ambiguous business goals into MVP roadmaps.
• Critical thinking: identifying data/assumption biases and proposing mitigation strategies.
   

What We Offer:

• Flexible part-time / contract work.
• Fast decision-making and low bureaucracy.
• Influence over architecture and stack choices.
• Remote collaboration and flexible schedule.
• Paid R&D participation (by agreement).

About Keymakr

• Keymakr specializes in end-to-end dataset preparation, including video and image annotation and labeling for AI projects.
  We take a personalized approach, delivering customized solutions tailored to each client’s needs.
   
• Our strong in-house R&D team develops and adapts annotation tools for every client, allowing us to successfully handle even the most complex requirements.

About the Role:

• We are looking for a Data Management Specialist to manage, process, and maintain large volumes of data and media assets used in AI and technology projects.
   
• This role focuses on ensuring data integrity, consistency, and availability while optimizing workflows through automation and close collaboration with cross-functional teams.
   

Key Responsibilities:

1. Manage and manipulate data using Linux file systems and native OS tools;
2. Work with media files, including video formats, codecs, resolution, and frame rate (FPS);
3. Verify data integrity using checksum and verification tools;
4. Pack and unpack large data archives efficiently;
5. Automate data-related workflows using scripting languages;
6. Prepare, structure, and maintain datasets in structured formats;
7. Maintain cloud-based data storage and perform data migration;
8. Collaborate with Project, QA, and Technical teams to ensure smooth data operations;
9. Clearly and consistently document workflows, processes, and task updates.

Technical Skills:

1. Strong knowledge of Linux file systems and administration (Ubuntu/Debian, 2+ years);
2. Experience with Windows OS administration;
3. Proficiency in Python and Bash / Shell scripting;
4. Experience with archive management tools for large datasets;
5. Knowledge of checksum verification tools (md5sum, sha256sum);
6. Tools for checking video properties, codecs, and formats;
7. Solid knowledge of JSON and CSV formats;
8. Experience with cloud storage platforms: AWS S3, Google Cloud Storage, or Azure;
9. Familiarity with JIRA, Slack, Confluence, and Google Docs.

Nice to Have:

1. Experience with point clouds / 3D data;
2. Strong understanding of complex technical specifications;
3. Experience working with large-scale datasets;
4. English & Communication;
5. English level B2 (Upper-Intermediate) or higher;
6. Ability to read and understand technical documentation and project requirements;
7. Clear written communication in English (reports, updates, documentation);
8. Participation in English-language meetings when required. 

If you want, I can also:

• tailor it for remote / hybrid / office format,
• shorten it to fit DOU’s compact job posting style,
• or adjust it to a more technical / more business-oriented tone.

Company Description

Agri/fintech platform that helps financial institutions and agricultural stakeholders make smarter lending and farm management decisions by turning real-time crop and field data into actionable insights. The platform combines advanced crop monitoring, expert agronomic knowledge, and data analytics to reduce risk, improve productivity, and enhance access to finance in agricultural markets, particularly in emerging regions.

Project Description

We are developing a Computer Vision-based orchard analytics system that uses 15 cm RGB satellite imagery (Vantor) to segment perennial orchards into tree canopies, crop rows, and in-row spaces. The generated segmentation data is transformed into GIS-ready polygons and agronomic metrics such as row orientation, spacing and length, canopy coverage, row and tree counts, and missing-tree detection. The solution is being built as an agrifintech MVP with the objective of supporting scalable global deployment.

Role Overview

We are looking for a Computer Vision Specialist / Data Scientisе to design, train, and deploy a semantic segmentation model for orchard structure detection from high-resolution satellite imagery.

This is a hands-on role focused on:

• Semantic segmentation modeling
• Large-scale geospatial raster processing
• Model experimentation and optimization
• Integration with a GIS analytics pipeline

You will work closely with the Technical Lead (GIS & Remote Sensing), Labeling Specialist, and Backend/DevOps support.

Required Qualifications

• 2+ years of experience in Computer Vision.
• Strong proficiency in PyTorch (preferred) or TensorFlow.
• Hands-on experience with semantic segmentation architectures.
• Experience working with high-resolution imagery (satellite, drone, or aerial).
• Experience with semi-supervised learning.
• Experience with geospatial data. Familiarity with geospatial coordinate systems and projections.

Technical Environment

• PyTorch + segmentation frameworks
• GDAL / Rasterio
• LabelMe for annotation
• PostGIS for spatial storage
• Cloud GPU infrastructure (RTX 3090 / T4 / A100 class)
• Satellite imagery (15 cm RGB Vantor)

Key Responsibilities

Model Development

• Design and implement semantic segmentation models (DeepLabv3+, U-Net or similar).
• Train models on tiled 15 cm RGB satellite imagery (512×512 px tiles).
• Implement augmentation strategies and loss functions for structured agricultural scenes.
• Run hyperparameter tuning and experiment tracking.
• Improve model performance using pseudo-labeling and semi-supervised techniques.

Data & Pipeline Engineering

• Build preprocessing pipelines for raster tiling and dataset preparation.
• Work with GDAL / Rasterio outputs and georeferenced datasets.
• Implement sliding-window inference over large mosaics.
• Optimize inference performance for large-area batch processing.

Geospatial Integration

• Support mask post-processing (morphology, skeletonization, polygon extraction).
• Collaborate on the derivation of row centerlines and structural metrics.
• Prepare outputs compatible with GeoTIFF, GeoJSON, and PostGIS.

Evaluation & Validation

• Define and track segmentation metrics (IoU, precision/recall).
• Perform structured validation at the field and row level.
• Work with agronomic stakeholders to validate meaningful outputs.

What Success Looks Like (MVP)

• Stable segmentation of orchard row structure across Uzbekistan pilot region.
• Accurate row centerline extraction.
• Reliable row-level agronomic metrics.

Inference pipeline scalable to large areas with minimal manual intervention.    

Working conditions

Mon – Fri 9-5 (Uzbekistan time)

Job Description

- High attention to detail and commitment to annotation accuracy.
- Comfort working with 2D/3D medical imaging data.
- Experience with or willingness to learn tools like 3D Slicer or similar.
- Background in medicine, radiology, biomedical sciences, anatomy, or related healthcare fields is a big plus.
- Preferred previous experience annotating CT or X-ray images.
- Experience working in healthcare AI, research, or imaging is a plus.

Job Responsibilities

As a Data Annotator, you will be responsible for labeling and annotating medical imaging data (CT and X-ray) to support the development of high-accuracy AI models. This role requires a high level of precision, consistency, and the ability to interpret anatomical structures. The data annotators would be trained by subject matter experts. A medical or healthcare background is a strong advantage.

- Annotation of Medical Images: Accurately label bones, joints, and anatomical landmarks on CT scans and X-ray images.
- Landmark Tagging: Identify clinically relevant anatomical landmarks that will be used in AI-based landmark detection.
- Quality Assurance: Follow strict medical annotation protocols provided by supervising doctors.
- Collaboration: Work closely with medical experts, AI engineers, and fellow annotators in a multidisciplinary environment.

Department/Project Description

We are developing advanced machine learning pipelines for the 3D reconstruction of bones from CT images, segmentation of anatomical structures, and detection of medical landmarks from CT scans and X-ray images. Accurate annotations are essential to the performance of our models. To ensure clinical precision, data annotators will be trained and guided by an experienced medical doctor.

Job Description

• High attention to detail and strong commitment to annotation accuracy.
• Proven experience using CVAT/Encord/Dataloop, or similar professional medical data annotation platforms.
• Experience in healthcare AI or medical imaging projects is a plus.
• Experience annotating laparoscopic or endoscopic surgical videos is a plus.
• Ability to identify key abdominal anatomical structures and/or a background in medical, surgical, and anatomy fields.

Job Responsibilities

• Annotate and segment anatomical structures in laparoscopic surgery videos.
• Tag surgical phases (dissection, clipping, cutting, extraction).
• Identify and label critical structures and risk zones.
• Follow medical annotation protocols defined by supervising surgeons.
• Collaborate with medical experts and AI engineers to ensure high-quality outputs.

Department/Project Description

Development of AI models for surgical phase recognition and anatomical structure detection during laparoscopic gallbladder removal procedures.
Annotators will receive structured training and continuous feedback from medical experts.