Jobs Data Science

Project

The project focuses on a deep refactoring and optimization of the reporting ecosystem combining Salesforce CRM and Tableau. The main goal is to redesign current data flows, replacing the bulky and slow architecture with a modular system of reusable components. You will work on a process redesign to ensure speed, data accuracy, and ease of future system maintenance.

Responsibilities

• Development and Redesign: designing and supporting complex interactive visualizations in Tableau Desktop with a focus on modularity.
• Architecture Optimization: transforming bulky data flows into a fast and stable reporting system.
• Integration and Setup: working with data streams from Salesforce CRM and ERP systems (SAP, Odoo, MS Business Central).
• Technical Optimization: performance tuning (Data Extracts, calculated fields, SQL query optimization).
• Business Collaboration: gathering requirements from stakeholders and translating them into clear technical specifications for visualization.
• Data Governance: ensuring data accuracy, integrity, and security (including Row-Level Security).

Requirements

• Must-have:
  • 1.5-2+ years of commercial experience with Tableau (Desktop, Server/Cloud) or other BI tools.
  • Strong SQL skills (writing complex queries, joins, CTEs).
  • Understanding of CRM and ERP system architecture (how data is stored and interconnected).
  • Intermediate+ English level (for technical communication and documentation).
• Nice-to-have:
  • Experience with Tableau and Salesforce integration (a significant plus).
  • Experience with Tableau Prep or other ETL tools (Alteryx, KNIME).
  • Tableau Desktop Specialist or Certified Data Analyst certification.
  • Experience with cloud data warehouses (Snowflake, BigQuery, AWS Redshift).

Conditions

• Collaboration Type: Part-time (Freelancer / Contract).
• Location: Remote.
• Schedule: Flexible (details to be discussed based on your availability).

Selection Process

1. Recruiter screening.
2. Technical interview with the Tech Lead.
3. Offer.

Data Science UA is a service company with deep expertise in AI and Data Science. Our story started in 2016 with the first Data Science UA Conference in Kyiv, and since then, we’ve built one of the largest AI communities in Europe.

About the role:
We are looking for a hands-on Sr. Machine Learning / Computer Vision Engineer with strong practical experience in designing, training, evaluating, and improving vision systems for real-world applications. The ideal candidate combines solid theoretical ML/CV fundamentals with an engineering mindset and is comfortable taking ownership of the full pipeline, from data preparation and experimentation to model optimization and deployment.

Responsibilities:
- Design, train, evaluate, and improve machine learning and computer vision models for real-world production use cases
- Build and maintain end-to-end ML pipelines, including data collection support, preprocessing, feature engineering, training, validation, and inference
- Develop solutions across both computer vision and machine learning tasks depending on business needs
- Perform error analysis, diagnose model weaknesses, and drive improvements in accuracy, robustness, and reliability
- Select and justify appropriate methods and model architectures based on dataset characteristics, product constraints, and deployment requirements
- Work with challenging real-world data, including noisy, limited, imbalanced, or weakly labeled datasets
- Collaborate with engineering and product teams to translate business problems into practical ML solutions
- Contribute to production-ready development with a focus on maintainability, reproducibility, and performance

Requirements:
- 3–5+ years of professional experience in Machine Learning / Computer Vision
- Strong senior-level practical expertise in computer vision, machine learning, and applied model development
- Hands-on experience with tasks such as object detection, image classification, anomaly detection, feature-based approaches, or similar
- Solid understanding of classical ML methods, including tree-based models, linear models, feature engineering, model selection, and evaluation
- Strong Python skills and practical experience with PyTorch, OpenCV, and common ML tooling
- Experience building full ML workflows including data preparation, augmentation, preprocessing, training, validation, testing, and deployment handoff
- Strong understanding of model evaluation, debugging, overfitting, generalization, optimization, and experiment-driven improvement
- Ability to write clean, modular, maintainable code and independently own technical solutions
- Strong analytical thinking, engineering maturity, and problem-solving skills

Nice to Have:
- Experience in adjacent ML domains such as time-series, audio, or NLP
- Experience with embedding-based methods, metric learning, retrieval systems, or memory-bank approaches
- Practical experience with model optimization techniques such as quantization, pruning, distillation, or related efficiency-focused approaches
- Experience working with real-world industrial datasets and ambiguous problem formulations
- Knowledge of software engineering best practices, reproducible experimentation, and pipeline design

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

Role Overview

Nucleus AI is seeking an AI / ML Data Scientist to help build and scale an AI-backed Learning Management System (LMS) that delivers personalized, adaptive learning experiences.

This is a hands-on, product-focused role at the intersection of machine learning, data science, and learning technology. You will design and deploy intelligent systems that directly impact learner engagement, personalization, assessment, and outcomes.

If you enjoy turning real-world data into models that are used by real users—and iterating on them in production—this role is for you.

Key Responsibilities

Machine Learning & Modeling

• Design, develop, and deploy machine learning models that power:
  • Personalized learning paths
  • Content and course recommendations
  • Learner analytics and insights
  • Adaptive assessments and feedback
     
• Build models for:
  • Learner skill inference and knowledge tracing
  • Engagement, completion, and drop-off prediction
  • Automated assessment scoring and feedback
     

Data & Analytics

• Analyze large-scale learner behavior data to extract actionable insights
• Develop and maintain data pipelines, feature engineering workflows, and model evaluation frameworks
• Apply statistical analysis and experimentation (including A/B testing) to validate model performance and impact
   

Collaboration & Product Integration

• Work closely with product managers, engineers, and instructional designers to translate learning objectives into AI-driven solutions
• Integrate models into production systems via APIs, batch pipelines, or real-time inference
   

LLMs & Advanced Techniques

• Experiment with and integrate LLMs and NLP techniques for:
  • Content generation
  • Learner feedback
  • Intelligent learner support
     
• Monitor models in production for performance, bias, and drift, and continuously improve them
   

Documentation & Governance

• Document models, assumptions, experiments, and results to ensure transparency, reproducibility, and maintainability
   

Required Qualifications

• Bachelor’s or Master’s degree in Data Science, Computer Science, AI/ML, Statistics, or a related field
• Strong proficiency in Python and common ML libraries (e.g., scikit-learn, PyTorch)
• Solid understanding of:
  • Supervised and unsupervised learning
  • Feature engineering and model evaluation
  • Statistical analysis and experimentation
     
• Experience working with both structured and unstructured data
• Proficiency in SQL and working with large datasets
• Ability to clearly communicate complex technical concepts to non-technical stakeholders
   

Nice-to-Have Qualifications

• Experience building ML systems in ed-tech, LMS platforms, or learning analytics
• Familiarity with
  • Large Language Models (LLMs)
  • NLP
  • Recommendation systems
  • Knowledge graphs
• Experience deploying models to production environments
• Exposure to cloud platforms such as AWS, GCP, or Azure
• Understanding of learning science, instructional design, or assessment theory
• Experience with MLOps tools (model versioning, monitoring, CI/CD for ML)
   

What We Offer

• Opportunity to work on mission-driven AI that improves how people learn
• Ownership of ML systems used by real learners at scale
• A collaborative, cross-functional team culture
• Competitive compensation and benefits
• Flexible work location and schedule
• Continuous learning and professional growth opportunities

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.

Data Science UA is a service company with deep expertise in AI and Data Science. Our story started in 2016 with the first Data Science UA Conference in Kyiv, and since then, we’ve built one of the largest AI communities in Europe.

About the client and role:
We are looking for a Computer Vision/3D Vision Engineer for our client, who is the creator of the world’s leading patented mobile body scanning technology, driving retail innovation by providing personalized fit and size recommendations and virtual try-on solutions for shoppers. The company helps brands reduce returns while simultaneously increasing conversion rates and average order value (AOV).

Responsibilities:
- Design, train, and deploy computer vision systems from initial dataset preparation to production-ready models.
- Solve complex problems in object detection, segmentation, and pose estimation using custom-built solutions.
- Develop and optimize pipelines for 3D reconstruction, depth estimation, and point cloud processing.
- Evaluate and debug model performance, ensuring high accuracy and technical depth in every solution.
- Manage dataset annotation, curation, and model evaluation metrics to ensure robust performance.

Requirements:
- Hands-on experience with object detection, segmentation, tracking, and camera calibration. You should have a deep understanding of image geometry.
- Proficiency in at least one of the following: 3D reconstruction, Multi-view geometry, Photogrammetry, NeRF/Neural rendering, or SLAM.
- Expert-level knowledge of Python and frameworks like PyTorch or TensorFlow.
- Proven ability to build and explain a CV system end-to-end, including architecture choice and deployment logic.
- Critical thinking and the ability to understand why a model works (or doesn't), with a focus on experimentation and debugging.

About Cyberette
Cyberette is an AI-native cybersecurity company developing forensic-grade technology to detect digital manipulation across video, audio, and images. Our mission is to restore trust in digital communication and protect people, organizations, and institutions from deception in the AI era.

About the Role
We are looking for a Computer Vision Data Scientist with strong initiative and technical excellence to join our core development team. You’ll be responsible for designing, training, and optimizing computer vision models and architectures used in our deepfake detection and forensic analysis platform.

This role requires someone who thrives in fast-paced environments, loves solving complex problems, and brings both creativity and rigor to their work. You’ll collaborate closely with our founders and engineers to push the limits of multimodal AI for real-world impact.

Key Responsibilities

• Develop, train, and deploy computer vision models for image and video manipulation detection.
• Research and experiment with new architectures, model ensembles, and multimodal fusion techniques.
• Preprocess, clean, and manage large-scale datasets for training and evaluation.
• Optimize model accuracy, latency, and scalability for enterprise-grade performance.
• Contribute to the design of the model evaluation framework and internal benchmarking tools.
• Collaborate with other engineers and researchers to integrate models into production pipelines.
• Document methodologies, experiments, and findings clearly and systematically.

Requirements

• MSc/PhD in Computer Science, Machine Learning, Artificial Intelligence, or related field.
• Strong experience in computer vision (CNNs, Transformers, multimodal models).
• Proficiency in Python and deep learning frameworks (PyTorch, TensorFlow).
• Solid understanding of ML ops, dataset curation, and model evaluation.
• Proven experience building and training models from scratch.
• Excellent analytical and problem-solving skills.
• Self-driven, disciplined, and collaborative mindset.

What We Value
At Cyberette, we believe technology should serve people. We are a team of builders united by a shared sense of purpose — creating tools that make the digital world safer and more truthful. If you care deeply about impactful innovation and want your work to matter, you’ll feel at home here.

We value ownership, fast responses, and a goal-oriented mindset. Our environment is collaborative and fast-moving, and we appreciate people who communicate proactively, take initiative, and focus on delivering meaningful results.

 

Bonus Skills

• Experience with audio or multimodal analysis.
• Knowledge of adversarial attacks or deepfake generation methods.
• Experience in real-time inference or edge deployment.

We are looking for a Data Scientist to join the analytics team of a leading Life Sciences / Pharma insights agency. The role focuses on advanced data modelling, NLP analysis, and transforming complex datasets into meaningful insights for pharmaceutical clients.

This is a hands-on role for someone who enjoys working with real-world datasets, building models, and communicating insights to stakeholders in the healthcare and scientific space.

The position is remote, full-time, and initially offered as a 6-month contract with strong potential extension.

ROLE OVERVIEW

You will work closely with analysts, researchers, and client teams to develop data science solutions that support insight generation for pharmaceutical and healthcare projects.

The role combines advanced modelling, NLP analysis, and data analytics, with an emphasis on interpreting complex data and presenting results clearly to non-technical stakeholders.

KEY RESPONSIBILITIES

Data Science & Modelling

• Develop machine learning and statistical models to analyse structured and unstructured data
• Apply NLP techniques such as topic modelling, clustering, text classification, and sentiment analysis
• Work with large datasets from surveys, research, and digital sources

Advanced Data Analysis

• Perform exploratory data analysis and statistical investigations
• Identify patterns, trends, and meaningful signals in complex datasets
• Translate modelling results into actionable insights

Data Processing & Pipelines

• Build and maintain data processing workflows
• Work with ETL pipelines and data transformations
• Ensure data quality, validation, and consistency

Insight Communication

• Present analytical findings clearly to internal teams and clients
• Build visualisations and reports that communicate complex results effectively
• Collaborate with analysts and consultants to shape research outputs

AI & LLM Applications

• Experiment with LLMs and AI tools for text analysis, summarisation, and insight generation
• Use modern AI-assisted development tools to accelerate workflows

REQUIRED SKILLS

• Strong Python skills for data science and analytics
• Experience with machine learning and statistical modelling
• Practical experience with NLP techniques (topic modelling, embeddings, classification, clustering)
• Experience with data analysis libraries such as pandas, NumPy, scikit-learn
• Ability to work with messy real-world datasets
• Strong analytical thinking and problem-solving ability

• Ability to clearly explain complex analytical results

   

NICE TO HAVE

• Experience working in Pharma, Life Sciences, or Healthcare analytics
• Experience working in agencies or consulting environments
• Familiarity with LLMs or AI-assisted data analysis tools
• Experience with data visualisation tools (e.g. Tableau, Power BI, Python visualisation libraries)

IDEAL CANDIDATE

You are someone who:

• enjoys building models and analysing complex datasets
• understands how to turn modelling results into insights
• can communicate findings clearly to non-technical audiences
• is comfortable working in a fast-paced consulting environment

Data Science UA is a service company with strong data science and AI expertise. Our journey began in 2016 with uniting top AI talents and organizing the first Data Science tech conference in Kyiv. Over the past 9 years, we have diligently fostered one of the largest Data Science & AI communities in Europe.

About the client:
The company is a trailblazer in the world of data-driven advertising, known for its innovative approach to optimizing ad placements and campaign effectiveness through advanced analytics and machine learning techniques. Our mission is to revolutionize the advertising sector by enabling brands to reach their audiences more effectively.

About the role:
We are seeking an experienced and motivated Senior Data Scientist to join our dynamic team. The ideal candidate will have deep expertise in supervised learning, reinforcement learning, and optimization techniques. You will play a pivotal role in developing and implementing advanced machine learning models, driving actionable insights, and optimizing our advertising solutions.
This position is based in Ukraine. The team primarily works remotely, with occasional in-person meetings in the Kyiv or Lviv office.

Responsibilities:
- Develop and implement advanced supervised and reinforcement learning models to improve ad targeting and campaign performance.
- Collaborate with cross-functional teams to identify opportunities for leveraging machine learning and optimization techniques to solve business problems.
- Conduct extensive data analysis and feature engineering to prepare datasets for machine learning tasks.
- Apply optimization algorithms to enhance the effectiveness and efficiency of advertising campaigns.
- Evaluate and refine existing models to enhance their accuracy, efficiency, and scalability.
- Utilize statistical techniques and machine learning algorithms to analyze large and complex datasets.
- Communicate findings and recommendations effectively to both technical and non-technical stakeholders.
- Stay updated with the latest advancements in machine learning, reinforcement learning, and optimization techniques.
- Work with engineering teams to integrate models into production systems.
- Monitor, troubleshoot, and improve the performance of deployed models.
- Mentor junior data scientists and contribute to the continuous improvement of the data science practice within the company.

Requirements:
- 5+ years of experience in data science or machine learning roles, with a strong focus on supervised learning, reinforcement learning, and optimization techniques.
- Technical Skills:
- Proficiency in Python.
- Strong understanding of working with relational databases and SQL.
- Experience with machine learning libraries such as scikit-learn, TensorFlow, PyTorch, or similar.
- Deep understanding of statistical modeling and supervised learning algorithms (e.g., linear regression, logistic regression, decision trees, random forests, SVMs, gradient boosting, neural networks).
- Hands-on experience with reinforcement learning algorithms and frameworks like OpenAI Gym.
- Practical experience with optimization algorithms (linear, non-linear, combinatorial, etc.).
- Hands-on experience with data manipulation tools and libraries (e.g., pandas, NumPy).
- Familiarity with cloud services, specifically AWS, is a plus.
- Practical experience building and managing cloud-based ML pipelines using AWS services (e.g. SageMaker, Bedrock) is a plus.
- Education:
- Bachelor's or Master's degree in Computer Science, Statistics, Mathematics, Engineering, or a related field. A PhD is a plus.

Other Skills:
- Strong analytical and problem-solving skills.
- Excellent communication skills, with the ability to clearly articulate complex concepts to diverse audiences.
- Ability to work in a fast-paced environment and manage multiple priorities.
- Strong organizational skills and attention to detail.
- Ability to mentor and guide junior data scientists.
- Must be able to communicate with U.S.-based teams

The company offers:
- An opportunity to be at the forefront of advertising technology, impacting major marketing decisions.
- A collaborative, innovative environment where your contributions make a difference.
- The chance to work with a passionate team of data scientists, engineers, product managers, and designers.
- A culture that values learning, growth, and the pursuit of excellence.

Data Science UA is a service company with deep expertise in AI and Data Science. Our story started in 2016 with the first Data Science UA Conference in Kyiv, and since then, we’ve built one of the largest AI communities in Europe.

About the role:
We are looking for a hands-on Sr. Machine Learning / Computer Vision Engineer with strong practical experience in designing, training, evaluating, and improving vision systems for real-world applications. The ideal candidate combines solid theoretical ML/CV fundamentals with an engineering mindset and is comfortable taking ownership of the full pipeline, from data preparation and experimentation to model optimization and deployment.

Responsibilities:
- Design, train, evaluate, and improve machine learning and computer vision models for real-world production use cases
- Build and maintain end-to-end ML pipelines, including data collection support, preprocessing, feature engineering, training, validation, and inference
- Develop solutions across both computer vision and machine learning tasks depending on business needs
- Perform error analysis, diagnose model weaknesses, and drive improvements in accuracy, robustness, and reliability
- Select and justify appropriate methods and model architectures based on dataset characteristics, product constraints, and deployment requirements
- Work with challenging real-world data, including noisy, limited, imbalanced, or weakly labeled datasets
- Collaborate with engineering and product teams to translate business problems into practical ML solutions
- Contribute to production-ready development with a focus on maintainability, reproducibility, and performance

Requirements:
- 3–5+ years of professional experience in Machine Learning / Computer Vision
- Strong senior-level practical expertise in computer vision, machine learning, and applied model development
- Hands-on experience with tasks such as object detection, image classification, anomaly detection, feature-based approaches, or similar
- Solid understanding of classical ML methods, including tree-based models, linear models, feature engineering, model selection, and evaluation
- Strong Python skills and practical experience with PyTorch, OpenCV, and common ML tooling
- Experience building full ML workflows including data preparation, augmentation, preprocessing, training, validation, testing, and deployment handoff
- Strong understanding of model evaluation, debugging, overfitting, generalization, optimization, and experiment-driven improvement
- Ability to write clean, modular, maintainable code and independently own technical solutions
- Strong analytical thinking, engineering maturity, and problem-solving skills

Nice to Have:
- Experience in adjacent ML domains such as time-series, audio, or NLP
- Experience with embedding-based methods, metric learning, retrieval systems, or memory-bank approaches
- Practical experience with model optimization techniques such as quantization, pruning, distillation, or related efficiency-focused approaches
- Experience working with real-world industrial datasets and ambiguous problem formulations
- Knowledge of software engineering best practices, reproducible experimentation, and pipeline design

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

Data Science UA is a service company with deep expertise in AI and Data Science. Our story started in 2016 with the first Data Science UA Conference in Kyiv, and since then, we’ve built one of the largest AI communities in Europe.

About the role:
We are looking for a hands-on Sr. Machine Learning / Computer Vision Engineer with strong practical experience in designing, training, evaluating, and improving vision systems for real-world applications. The ideal candidate combines solid theoretical ML/CV fundamentals with an engineering mindset and is comfortable taking ownership of the full pipeline, from data preparation and experimentation to model optimization and deployment.

Responsibilities:
- Design, train, evaluate, and improve machine learning and computer vision models for real-world production use cases
- Build and maintain end-to-end ML pipelines, including data collection support, preprocessing, feature engineering, training, validation, and inference
- Develop solutions across both computer vision and machine learning tasks depending on business needs
- Perform error analysis, diagnose model weaknesses, and drive improvements in accuracy, robustness, and reliability
- Select and justify appropriate methods and model architectures based on dataset characteristics, product constraints, and deployment requirements
- Work with challenging real-world data, including noisy, limited, imbalanced, or weakly labeled datasets
- Collaborate with engineering and product teams to translate business problems into practical ML solutions
- Contribute to production-ready development with a focus on maintainability, reproducibility, and performance

Requirements:
- 3–5+ years of professional experience in Machine Learning / Computer Vision
- Strong senior-level practical expertise in computer vision, machine learning, and applied model development
- Hands-on experience with tasks such as object detection, image classification, anomaly detection, feature-based approaches, or similar
- Solid understanding of classical ML methods, including tree-based models, linear models, feature engineering, model selection, and evaluation
- Strong Python skills and practical experience with PyTorch, OpenCV, and common ML tooling
- Experience building full ML workflows including data preparation, augmentation, preprocessing, training, validation, testing, and deployment handoff
- Strong understanding of model evaluation, debugging, overfitting, generalization, optimization, and experiment-driven improvement
- Ability to write clean, modular, maintainable code and independently own technical solutions
- Strong analytical thinking, engineering maturity, and problem-solving skills

Nice to Have:
- Experience in adjacent ML domains such as time-series, audio, or NLP
- Experience with embedding-based methods, metric learning, retrieval systems, or memory-bank approaches
- Practical experience with model optimization techniques such as quantization, pruning, distillation, or related efficiency-focused approaches
- Experience working with real-world industrial datasets and ambiguous problem formulations
- Knowledge of software engineering best practices, reproducible experimentation, and pipeline design

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

If you’re a talented Data Scientist looking to be part of a fast-growing, innovative R&D team, shape the future of decision-making in business, work on cuttingedge technologies and vast amounts of global real-time data, affect billions of mobile users, and become an industry leader - then let’s talk! Start.io is a mobile data platform that enables organizations to uncover insights and make datadriven decisions that enhance strategies and drive growth. We are seeking a Senior Data Scientist to join our Data Science & Engineering team.

Responsibilities:

• Analyze large and complex data to discover hidden patterns and opportunities;
• Develop and own end-to-end highly scalable data pipelines;
• Develop state-of-the-art machine learning models, and algorithms and build solutions in monetization fields;
• Provisioning data models for real-time (online) processing;
• Shape new data products, and constantly improve the existing products;
• Collaborate with multiple teams including Product, BI, Analysts, DevOps, R&D and Marketing to lead and accomplish overall solutions;
• Oversee a complete release to production, including analyzing requirements, testing, monitoring and measuring results, multitasking, and quickly responding to critical issues.

Requirements:

• B.Sc./M.A/M.Sc. degree in Computer Science, Engineering, Math, Statistics, or other equivalent fields;
• 6 years as a Data Scientist - a strong background in ML concepts and models;
• Hands-on experience with Python, especially with data science ML libraries (regression & classification algorithms e.g. XGB or LGBM, recommendation system algorithms, etc.;
• Understanding of databases and SQL for data retrieval;
• Monitoring and alerting tools (e.g. Grafana, Kibana).

Will be a plus:

• Knowledge in the programmatic advertising landscape – DSPs, SSPs, exchanges – significant advantage;
• Experience with relational (e.g. Vertica, VoltDB) and non-relational (e.g. MongoDB) databases;
• Experience with PMML.

What we offer:

• Help and support from our caring HR team;
• 20 days of vacation;
• The exchange of experience and work with talented colleagues;
• Last but not least - valuable compensation for your efforts.

DeepX is looking for a Computer Vision Engineer to develop high-precision 3D reconstruction systems based on polarization imaging.

In this role, you will work at the intersection of computer vision, optical physics, and geometry, building algorithms that extract detailed surface information from polarimetric data using Shape-from-Polarization (SfP) techniques.

Core requirements:

● Strong understanding of polarized light physics and Stokes vector representation;

● Solid knowledge of linear algebra and vector calculus for coordinate transformations and 3D geometry;

● Experience implementing algorithms in Python (NumPy, SciPy);

● Practical experience with computer vision libraries such as OpenCV or scikit-image;

● Ability to extract reliable signals from noisy imaging data.

Nice to have:

● Experience with Shape-from-Polarization (SfP) or other Shape-from-X methods;

● Understanding of surface normals, depth maps, and 3D reconstruction pipelines;

● Experience working with polarization cameras;

● Knowledge of light reflection and scattering on different materials;

● Experience with normal integration methods for surface reconstruction.

Our Customer:

Our customer is a global, data-driven digital advertising technology company operating at large scale across mobile apps and web platforms.
 

Your Tasks:

• Analyze large, complex datasets to uncover patterns, insights, and business opportunities;
• Design, build, and own scalable, end-to-end data pipelines;
• Develop and productionize advanced machine learning models and algorithms, particularly in monetization-related domains;
• Build and maintain data models for real-time (online) processing;
• Contribute to the creation of new data-driven products and continuously improve existing ones;
• Collaborate with Product, BI, Analytics, DevOps, R&D, and Marketing teams to deliver complete solutions;
• Lead full production releases, including requirements analysis, testing, monitoring, result evaluation, and rapid response to critical issues.
   

Required Experience and Skills:

• 6+ years of experience as a Data Scientist with a strong foundation in machine learning concepts and models;
• Previous experience in the Ad-Tech industry;
• Hands-on experience working on large-scale, high-load systems processing billions of requests per second;
• Hands-on experience with Python including writing production-ready code and working with modern Python frameworks and tooling;
• Experience with data science / ML libraries and models, such as regression and classification models (e.g. XGBoost, LightGBM), recommendation systems, and related ML techniques;
• Solid understanding of databases and SQL for data analysis and retrieval;
• Experience with monitoring and alerting tools such as Grafana and Kibana;
• Proven experience working in real-time / online environments;
• Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, Statistics, or a related field;
• Strong end-to-end ownership mindset: ability to take solutions from research and experimentation through implementation and production;
• Fluent English with strong communication and collaboration skills.
   

Would Be a Plus:

• Familiarity with the programmatic advertising ecosystem (DSPs, SSPs, ad exchanges);
• Experience with relational databases (e.g. Vertica, VoltDB) and non-relational databases (e.g. MongoDB);
• Experience working with PMML.

Working conditions

5-day working week, 8-hour working day;

Remote work.

Job Description

• Master’s degree in Computer Science, Data Science, Applied Mathematics, or a related field.
• 7+ years of professional experience in machine learning, data science, or AI engineering.
• Proven experience as a technical lead or solution architect for ML/AI projects, with accountability for end-to-end delivery in a production environment.
• Strong proficiency in Python and the modern ML/AI ecosystem (e.g., PyTorch, Hugging Face, LangChain/LangGraph, Scikit-learn).
• Hands-on experience with data ingestion, RAG pipeline optimization, model evaluation, deployment (MLOps), and monitoring.

• Deep understanding of generative AI (LLMs, embeddings, RAG, prompt engineering, and agentic reasoning) and its practical constraints (latency, cost, safety, hallucinations).

   

Job Responsibilities

• Work with stakeholders to translate domain-specific knowledge into "Spec-Driven" ML architectures and agentic workflows.
• Design and implement solutions that combine the reasoning power of LLMs with the precision of structured knowledge (ontologies/knowledge graphs).
• Pilot the Claude Code CLI and other agentic tools to generate code, run automated tests, and maintain "Context Hygiene" within the project repository.
• Apply structured 4-phase debugging to ML pipelines, focusing on root-cause analysis of hallucinations, retrieval failures, and data drift.
• Define and automate "Skills" (prompt libraries, evaluation scripts, and deployment templates) to be re-used across multiple AI-Native pods.
• Define and implement quality gates, safety metrics, and cost-control practices for GenAI components.

• Lead by example in adopting AI-Native practices, mentoring the AI Apprentice and other team members in the "People + Agents" delivery model.

   

Department/Project Description

We are seeking a Senior ML / Data Science Lead to join our AI & Data practice and spearhead a next-generation initiative within our AI-Native Delivery Pod. In this role, you will combine high-end machine learning engineering with a revolutionary approach to software delivery. You will not just build AI; you will build AI using AI, orchestrating agentic workflows to deliver complex solutions at a velocity traditional teams cannot match.

You will focus on building practical, production-ready solutions that bridge the gap between unstructured data and structured logic using LLMs, agentic workflows, and knowledge representations (ontologies, knowledge graphs). You are expected to be an "AI-Native" pioneer, utilizing tools like Claude Code, Cursor, and custom MCP servers to automate your own development lifecycle—from data exploration and model evaluation to code generation and documentation.

Remote – Full-time – Flexible Hours - Shifted schedule to EST (New York) timezone

About TenViz

TenViz Predictive Analytics builds AI-powered solutions that help Fortune 100 investors anticipate market shifts before they become consensus. We combine financial expertise, mathematical rigor, and AI research to deliver predictive analytics for institutional investors.

Your Expertise

• Strong interest in quantitative and algorithmic approaches
• BSc/MSc in Math, Physics or related field with solid background in statistics, linear algebra and probability theory
• Advanced Linear Algebra, Advanced Statistics, Using Linear Algebra methods in Statistical analysis
• 2+ years in data research, quantitative modeling, ML prototyping, and backtesting
• Skilled in Python, SQL, Git
• Experience with  experiment tracking, pipeline orchestration and workflow automation

Nice to have:

• Bayesian inference, reinforcement learning, or ensemble methods
• Financial econometrics, time-series forecasting, or regime modeling

What You’ll Do

• Design predictive models using probabilistic, ML, and hybrid approaches
• Analyze large-scale financial datasets to detect hidden patterns and early regime shifts
• Prototype and evaluate algorithms, from deep learning to Bayesian models
• Write production-grade research code in Python/SQL that integrates into pipelines
• Translate results into insights and themes investors can act on — not just outputs

Example projects:

• Forecasting inflation via commodity price signals
• Constructing probabilistic forecasts of currency moves around macroeconomic announcements
• Designing a Bayesian model to estimate recession probabilities from yield curve dynamics
• Designing a regime-aware ML pipeline that switches models depending on market conditions
• Creating hybrid ensembles that outperform traditional benchmarks

What’s in It for You

• Remote-first, flexible hours
• Paid vacation & holidays (after 8 months)
• Continuous learning: advanced ML/DL workshops, probabilistic methods, optimization
• Supportive, collaborative team — no micromanagement, focus on trust and creativity
• Work on real financial problems where your models influence billion-dollar decisions