Jobs Data Science

We are looking for an analytically strong and technically curious Data Scientist to join our team in Lviv.

What you will do:

Analyse large and complex datasets to identify patterns, relationships, and opportunities for improving our models.

Develop, test and improve algorithms and predictive models using Python.

Work with geospatial data and contribute to the development of high-resolution environmental maps.

Apply statistical methods and machine learning where appropriate.

Contribute to monitoring, troubleshooting, and improving our existing data processing and production systems.

Investigate data quality issues, unexpected model outputs and processing failures.

Gradually develop a good understanding of our Azure-based production environment.

Work independently and collaboratively to solve analytical and technical challenges.

What we are looking for:

Approximately 3–5 years of relevant professional experience.

Strong analytical thinking and problem-solving abilities.

Solid programming skills in Python, including experience with Pandas, NumPy or similar libraries.

Good understanding of statistics, data analysis and quantitative modelling.

Experience working with complex datasets and developing reliable, maintainable code.

Ability and willingness to understand existing software systems, investigate errors and contribute to troubleshooting.

A structured, independent and pragmatic approach to solving problems.

Good communication skills in English.
Nice to have:

Experience with geospatial analysis, GIS or map production, using tools such as GeoPandas, Rasterio or QGIS.

Experience with machine learning and predictive modelling, for example scikit-learn.

Familiarity with Microsoft Azure or other cloud platforms.

Experience with automated data pipelines, monitoring or production environments.

Experience with satellite imagery, remote sensing or environmental data.

Familiarity with Git and SQL.

What we offer:

The opportunity to work with satellite data, environmental modelling and high-resolution geospatial technologies.

Challenging analytical problems with practical, real-world applications.

A small, international team where your work will have a direct impact.

Opportunities to develop expertise in data science, geospatial analysis and production systems.

A varied role combining research, model development and practical problem-solving.

We are looking for a Computer Vision Engineer with strong experience in deep learning and applied computer vision systems.
The role involves working on challenging CV problems in real-world environments.
We consider Middle and Senior engineers, with scope adapted to experience.
Responsibilities

• Develop, train, and optimize deep learning models for:
  • image retrieval
  • image matching (keypoint detection and matching)
  • auxiliary perception tasks supporting the main pipeline
• Evaluate models using quantitative metrics, including:
  • retrieval quality (mAP, Recall@K)
  • matching performance (precision/recall, repeatability)
  • end-to-end system metrics (accuracy, latency, robustness)
• Optimize models for production deployment using modern toolchains:
  • ONNX / TensorRT / OpenVINO / edge acceleration where applicable
  • model compression techniques (quantization, pruning, distillation)
  • latency, memory, and throughput optimization
• Work with large-scale visual datasets and descriptor-based representations
• Collaborate with engineering teams to integrate models into production systems

Required Qualifications

• 2–4+ years of experience in Computer Vision / Deep Learning
• Hands-on experience with keypoint detection and matching models (e.g. SuperPoint, R2D2, DISK, LightGlue, SuperGlue)
• Experience with image retrieval or metric learning systems
• Strong understanding of geometric and motion-related computer vision concepts:
  • keypoint detection and description
  • image matching and geometric verification (RANSAC, homography, PnP)
  • pose estimation and refinement techniques (PnP, bundle adjustment, pose graph optimization)
  • optical flow and frame-to-frame tracking
  • vector search / ANN methods for descriptor retrieval
• Strong Python skills (PyTorch and scientific computing stack)
• Ability to read and understand inference code in C++

Nice to Have

• Experience with noisy or imperfect real-world datasets
• Self-supervised or unsupervised learning methods (contrastive learning, homography supervision, etc.)
• Experience optimizing models for edge deployment (quantization, pruning, distillation)
• Familiarity with FAISS or similar vector search systems
• Experience with optimization libraries for geometric problems (bundle adjustment, pose refinement)
• Understanding of real-time constraints (latency, memory, CPU inference budgets on ARM or low-power x86 systems)

We’re building an internal platform for AI agents that take on real work across the company, from operations to finance and HR. The first version is already running. We’re looking for an engineer to take it to production, grow it, and own it.

Responsibilities

• Take the platform to production. Turn the agent studio from a working prototype into a stable product the company relies on, and own its architecture
• Build the core infrastructure. Develop the LLM gateway, the connector service, and support for skills and MCP, so agents can use models, tools, and business systems safely
• Grow the connector library. Add and maintain connections to the systems our teams work in, starting with what Operations needs most
• Run the software factory. Keep the AI coding agents shipping routine tasks and bug fixes, and improve how work flows through it
• Keep it secure and reliable. Own access control, SSO, audit, monitoring, and on-call for everything you build
• Work with the people who use it. Onboard new users, support the business analysts who build agents, and turn their feedback into the roadmap

Required Skills & Experience

• Experience shipping production systems and being on call for them
• Strong TypeScript skills
• Hands-on experience building with LLMs beyond the demo: agents, tool calling, and evals
• Solid understanding of security fundamentals: access control, SSO, and audit
• Drive for ownership and the ability to do your best work where there’s no playbook yet

Nice to Have

• Experience with durable workflow engines
• Experience building no-code or workflow builders
• Security or compliance work on AI systems

We offer:

• Official employment in accordance with Ukrainian labor law.
• Military service deferment.
• Modern working equipment.
• Regular performance reviews to support your professional growth.
• 24 working days of paid vacation annually.
• Paid sick-leaves.
• English language classes.
• Compensation for educational and training programs.
• Team buildings and corporate events in a friendly and collaborative environment.

The project is for one of the world's famous science and technology companies in pharmaceutical industry, supporting initiatives in AWS, AI and data engineering, with plans to launch over 20 additional initiatives in the future. Modernizing the data infrastructure through the transition to Snowflake as a priority, as it will enhance capabilities for implementing advanced AI solutions and unlock numerous opportunities for innovation and growth.

We are seeking a Senior Snowflake Developer who will be responsible for designing, developing, and maintaining scalable data solutions that effectively meet the needs of our organisation.

The role will serve as a primary point of accountability for the technical implementation of the data flows, repositories and data-centric solutions in your area, translating requirements into efficient implementations.

• Responsibilities:

  • Design and work with Snowflake databases, schemas, views, and RAW, STG, and MART data layers.
  • Manage access using RBAC, role hierarchies, row-level security, and column-level access policies.
  • Work with dynamic tables, external stages, Snowpipe, and batch data loading.
  • Build and support reliable, production-grade Snowflake data pipelines.
  • Write optimised SQL queries for data extraction, transformation, and loading.
  • Utilise Python for advanced data processing, automation tasks, and system integration.
  • Be an advisor with your In-depth knowledge of Snowflake architecture, features, and best practices.
  • Develop and maintain complex data pipelines and ETL processes in Snowflake.
  • Collaborate with data architects, analysts, and stakeholders to design optimal and scalable data solutions.
  • Automate DBT Jobs & build CI/CD pipelines using Azure DevOps for seamless deployment of data solutions.
  • Ensure data quality, integrity, and compliance throughout the data lifecycle.
  • Troubleshoot, optimize, and enhance existing data processes and queries for performance improvements.
  • Document data models, processes, and workflows clearly for future reference and knowledge sharing.
  • Build Data tests, Unit tests and mock data frameworks
  • Good to have AWS and Streamlit knowledge

• Mandatory Skills Description:

  • Master’s degree in computer science, Information Technology, or a related field.
  • 5+ years of overall experience in data engineering, including relevant and hands-on expertise in Snowflake and DBT.
  • Extensive hands-on experience in writing complex SQL queries, demonstrating proficiency in data manipulation and analysis for large data volumes.
  • Strong understanding of data warehousing concepts, methodologies, and technologies with in-depth experience in data modelling techniques (OLTP, OLAP, Data Vault 2.0)
  • Experience building data pipelines using DBT (Data Build Tool) for data transformation.
  • Familiarity with advanced techniques for performance tuning methodologies in Snowflake including query optimization.
  • Strong knowledge with CI/CD pipelines, preferably in Azure DevOps.
  • Excellent problem-solving, analytical, and critical thinking skills.
  • Strong communication, collaboration, and interpersonal skills.

Department/Project Description

The project focuses on developing and modernizing a global data platform that supports data-driven marketing and advertising analytics for a leading international company.
The goal is to improve data quality, scalability, performance, and the reliability of data processing.

Job Description

• Familiarity with Google Cloud Platform, including BigQuery, Cloud Composer (Airflow), Compute Engine, and Monitoring.
   AWS or Azure experiecne could be also considered.
• Strong proficiency in Python, including experience in building, testing, and deploying ETL/ELT pipelines in production
   Experience developing data quality frameworks, including automated tests, cross-dataset validations, and anomaly detection across diverse data assets
• Advanced SQL skills, including data modeling (star/snowflake/3NF), optimization, and writing performant queries tailored to BigQuery’s slot-based execution model
• Solid working knowledge of DBT, including incremental models, testing, documentation, and advanced features like custom macros
• Demonstrated ability to optimize BigQuery workloads through partitioning, clustering, materialized views, and cost-aware development practices
• Experience working with ETL orchestration tools, ideally Cloud Composer or similar frameworks (Kestra, Dagster, etc.).
   Hands-on experience consuming and managing APIs for data extraction.
•  Exposure to Site Reliability Engineering (SRE) best practices, including ticket triage, incident management, and documenting runbooks/SOPs
• Familiar with Git and modern software development workflows, including pull requests and code reviews
• Comfortable working in an agile team environment, with the ability to multitask and reprioritize based on changing project needs
• English speaking and writing B2+, with the ability to engage technical and non-technical stakeholders alike
   

Job Responsibilities

•  Designing, building, and maintaining scalable, reliable data pipelines using Python,SQL, DBT, and Google Cloud Platform (GCP) services like BigQuery and Cloud Composer.
• Contributing to the development and evolution of our data quality framework, ensuring robust automated testing and cross-dataset validation across all critical data assets
• Writing and optimizing advanced SQL to power data models, analytics, and reporting pipelines, with a focus on performance and efficiency within BigQuery
• Developing and maintaining DBT models, including testing, documentation, incremental loading strategies, and the creation of reusable macros
• Supporting day-to-day incident and ticket resolution, including root cause analysis and documentation of runbooks/SOPs to drive platform reliability
• Working with APIs and external data sources to extract, normalize, and integrate new datasets into the platform
• Participating in agile ceremonies (stand-ups, retros, planning), contributing to sprint goals and helping support the wider team objectives
• Actively contributing to code reviews, documentation, and peer learning - helping to raise engineering standards and share knowledge within the team
• Monitoring system health and pipeline performance using GCP-native tools and dashboards, and proactively identifying opportunities for cost or performance optimization

Gamzix is a high-growth international licensed iGaming provider, creating high-performing games and scalable, cutting-edge solutions for the global market.
Our team of 150+ specialists operates globally, with Warsaw as our main hub office. We are driven by continuous improvement and a people-first culture where we communicate openly, win together, and truly enjoy the game we’re building.
We operate under an MGA B2B Critical Supply License and certifications from iTechLabs and GLI, ensuring our products meet high standards of quality, compliance, and performance across markets.

We are looking for a Game Mathematician to create, calibrate, deeply tune, and validate mathematical models for our slot games. We are scaling our mathematical department to transition to a dedicated structure, assigning one dedicated mathematician to each of our game streams. 

Responsibilities

• Develop and design mathematical models for slot games from initial concept to release
• Collaborate closely with Game Producers to refine, balance, and tune game concepts
• Validate, simulate (alongside simulation developers), and sign off on mathematical models to ensure they perform strictly as intended
• Troubleshoot with engineers on the precise technical implementation and integration of math models
• Maintain structured documentation and records of game math files, configurations, and simulation benchmarks
• Provide expert math support and clear explanations for game designers, engineers, compliance, and other internal teams
• Deliver early feedback and predictions on player engagement based on the game's mathematical profile
• Design and propose original features and mathematical solutions for diverse game mechanics
• Analyze BI performance data of live games to continuously tune and optimize mechanics based on real metrics
• Mentor and support junior team members, guiding them through technical decisions during math creation

Requirements

• 2–3 years of practical commercial experience creating mathematical models specifically for slot games (experience in game providers, dedicated slot studios, or outsource)
• Deep knowledge of probability theory, combinatorics, RTP calculation, volatility, and mechanics balancing
• Proficiency in specialized mathematical modeling tools, simulation software, and working with math files
• Hands-on experience with Python and C# for calculations and simulations
• Experience working with BI analytics to evaluate product performance
• Perfectionism and strong attention to detail: a drive to bring models to perfection, deeply tune, and polish the math
• High level of autonomy with the ability to conduct calculations and manage your workflow independently without micromanagement
• Solid communication skills for regular dialogue with the Game Producer and the wider team

Benefits

• 7-hour working day, 10:00 AM—6:00 PM, including a lunch break
• A-class office in Mokotów, Warsaw
• Competitive salary in the IT field, with taxes paid
• 10 days of paid sick leave
• 28 days of paid vacation: 18 days plus 10 additional days separately
• Paid state holidays
• Medical insurance
• Courses and training reimbursement
• Referral bonus for bringing talented people to the team
• Corporate English courses: business or general English, with morning and evening schedules
• People-oriented company with a focus on work-life balance
• Open and friendly communication
• Corporate team-building events and activities

Please note that feedback on your application will be provided within two weeks if a positive decision is made regarding your candidacy.

Project Description:

Join our Development Centre in Bucharest and become a member of our open-minded, progressive and professional team. In this role you will be working for one of our world-famous clients.

The Chief Security Office (CSO) of our client comprises the Chief Information Security Office (CISO) and the Corporate Security unit. The CISO organization guarantees information security for our client.

The role resides inside the Cyber Hygiene Engineering team within CSO. The team is responsible for developing and maintaining security solutions that improve the organization's security posture through automation, cloud security, vulnerability management, and application security.

We are looking for a Python-focused AI and data science engineer to help design, build, and improve practical AI automation workflows. This role is suited for someone who can combine strong software engineering discipline with applied machine learning, data analysis, and LLM-based tooling.

On top of attractive salary and benefits package, Luxoft will invest into your professional training, and allow you to grow your professional career.

Responsibilities:

• 4-5 years+ of experience

• Design, build, and maintain Python-based AI workflows, evaluation harnesses, and automation tools.

• Work with LLMs, AI agents, prompt pipelines, embeddings, and retrieval-augmented generation.

• Analyze structured and unstructured data to support automation, decision-making, and model evaluation.

• Create datasets, validation sets, benchmarks, and evaluation metrics for AI-assisted workflows.

• Integrate AI tools with existing business processes, internal data sources, and developer workflows.

• Improve reliability, observability, and repeatability of AI outputs.

• Document technical decisions, workflows, and assumptions clearly.

Mandatory Skills Description:

• Strong Python experience, including clean scripting, packaging, testing, and data processing.

• Practical experience using LLM APIs, AI agents, prompt engineering, or AI automation frameworks.

• Ability to design experiments, evaluate model outputs, and interpret results.

• Familiarity with notebooks, Git, virtual environments, and reproducible development workflows.

• Good software engineering habits, including tests, readable code, version control, documentation, and maintainability.

• Ability to turn ambiguous business or workflow problems into concrete technical prototypes.

Nice-to-Have Skills Description:

• Experience in a multinational environment would be a plus

ZentixSoft is looking for a Hands-on Technical Lead / Data Architect! 🚀

Format: Full-Time Outstaffing, 13-week engagement (First 3 weeks 100% on-site in Riyadh, followed by a hybrid schedule) with long-term extension options.

Location: Riyadh, Saudi Arabia (Open to candidates in KSA or willing to relocate).
 

🧩 Responsibilities:

• Architecture & Hands-On Engineering: Design HLD/LLD for Lakehouse architectures (raw, standardized, curated layers), Customer 360 target data models, and source-to-target mappings from Oracle Data Warehouses and Data Lakes while writing production SQL and Python code.
• GCP Infrastructure & Pipeline Ownership: Build and review pipelines using Google Cloud Platform (BigQuery, Cloud Storage, Dataflow, Cloud Run/GKE, Airflow, dbt).
• Identity Resolution & Quality: Implement deterministic/probabilistic matching rules, data quality frameworks, reconciliation, and lineage metadata.
• BI & Enterprise Integration: Build Power BI semantic models with Row-Level/Object-Level security, embed reporting into Dynamics 365, and set up AI-ready foundations (Vertex AI / BigQuery ML).
• Security & Compliance: Enforce security by design, encryption, audit logging, and KSA PDPL data residency standards.
   

🎓 Our Perfect Match (Requirements):

• Data Engineering & Architecture: 10+ years in Data Engineering/Architecture, with 3+ years of hands-on experience on Google Cloud Platform (GCP).
• Production Tooling: Proven production experience with BigQuery, Airflow, dbt, SQL, and Python.
• Domain Expertise: Strong background in Insurance or Banking data domains (policies, claims, customers, providers).
• Team Leadership: Experience leading technical teams of 6–8 engineers while maintaining hands-on contribution.
• On-Site Presence: Available for the first 3 weeks 100% on-site in Riyadh, transitioning to a hybrid setup afterwards.
   

➕ Nice to Have:

• Experience with restricted-data zones (e.g., medical data regulations).
• Hands-on knowledge of BigQuery ML, Gemini, or Vertex AI.
   

📍 Project Details:

⏳ Duration: 13 weeks + long-term extension.

📍 Location: Hybrid Riyadh (First 3 weeks full-time on-site).

Key responsibilities

• Own end-to-end solution architecture for data, analytics, and AI use cases, with accountability for design quality, security, scalability, supportability, and cost.
• Translate business needs into practical technical designs, target architectures, integration patterns, and delivery-ready implementation guidance.
• Lead by example through hands-on prototyping, architecture reviews, troubleshooting, code/design reviews, and technical support for delivery teams.
• Ensure security, governance, compliance, and operational readiness are built into solutions from design through production deployment.

Must have

• Bachelor's degree in Computer Science, Information Systems, Engineering, or a related field.
• 10+ years of IT experience, including 5+ years in senior solution architecture roles for enterprise data, analytics, and AI platforms.
• 5+ years strong hands-on experience with Azure-based data platforms, including Azure Data Factory, Databricks, data lake architectures, SQL, and dimensional data modeling, with the ability to build practical end-to-end solution designs.
• Strong experience with enterprise security, governance, and compliance, and the ability to work effectively with enterprise architects and guiding or regulatory functions to ensure solutions meet organizational standards and control requirements.
• Enterprise AI enablement experience: designing solutions with Azure OpenAI and retrieval-augmented (RAG) patterns.
• BI/analytics architecture experience with Power BI or a similar enterprise BI technology.

Advantages

• Deep hands-on expertise in Power BI architecture, including semantic models, DAX, Power Query, workspace strategy, governance, performance optimization, and enterprise reporting patterns.
• Experience with Microsoft-first AI platforms and services such as Azure AI Foundry, Copilot Studio, Azure AI Search, Semantic Kernel, Prompt Flow, and enterprise AI governance patterns.
• Experience with Microsoft Fabric, Azure DevOps, GitHub Enterprise, and CI/CD for Data or AI solutions.
• Experience with Microsoft security and governance services such as Microsoft Entra, Purview, and Key Vault.
• Experience in life sciences, pharmaceuticals, or other regulated industries.
• Relevant Microsoft certifications in Azure, Power BI, data, or AI architecture.

Key skills

• Hands-on technical leadership with a clear lead-by-example mindset.
• Strong architecture judgment across data, analytics, AI, integration, security, and non-functional requirements.
• Proven ability to own solution architecture end to end: shape target designs, validate them through prototyping or proof of concept where needed, guide delivery teams through implementation, and provide technical oversight through design reviews and production readiness checks.
• Ability to translate business problems into practical, delivery-ready technical solutions.
• Strong troubleshooting, decision-making, estimation, and risk management capability.
• Comfort working across distributed teams and external suppliers in enterprise delivery environments.
• Excellent communication skills in English, with the ability to drive alignment across architects, delivery teams, business stakeholders, and enterprise control functions.

We are looking for an experienced Quant Engineer to join our Trading Core and Risk Tech teams and take ownership of the mathematical models behind our trading and risk infrastructure. With billions in trading volume processed across our systems, accuracy, scalability, and model reliability are essential.

You will develop, backtest, and optimize the quantitative models powering our pricing engines, automated market-making strategies, risk management frameworks, including A/B/C-book optimization, and liquidation logic. Working closely with Core Product Managers and engineering teams, you will apply advanced mathematics, data analysis, and high-performance engineering to turn complex market data into scalable models and proprietary trading advantages.

Responsibilities

• Research, design, and prototype quantitative models for pricing, risk management, and market making
• Build and maintain robust backtesting frameworks to validate the performance and safety of models before production deployment
• Write clear, comprehensive mathematical and algorithmic specifications for Backend Engineers (Trading Core squads)
• Cross functional with the R&D and Trading Ops teams
• Conduct post-incident deep dives (e.g., after major market gaps or liquidations) to identify algorithm performance gaps and optimize them

Requirements

• 4+ years of experience as a Quant or Data Scientist
• Exceptional knowledge of probability theory, stochastic calculus, time-series analysis, and financial mathematics
• Advanced proficiency in Python (NumPy, Pandas, SciPy, SK-Learn, Statsmodels) for data analysis, modeling, and backtesting
• Experience with Machine Learning
• Deep understanding of market microstructure, order book dynamics, risk metrics (VaR, Expected Shortfall), and margin/liquidation mechanisms
• SQL skills and experience working with large-scale historical market data (tick data, order logs)
• Strong logical thinking, initiative, and well-developed communication skills

Will be a plus

• Experience in CFD, Crypto CEX, Prop Trading Firm, or Hedge Fund
• Degree (MSc or PhD preferred) in a highly quantitative field: Mathematics, Physics, Quantitative Finance, Statistics, or Computer Science
• Understanding of Asset pricing models (e.g., Black-Scholes, local volatility models, Greeks management)
• Knowledge of the MetaTrader platforms (MT4/MT5)
• Experience with AI (Claude.io, Copilot, Codex)

We offer

• 20 paid vacation days per year
• 10 paid sick leave days per year
• Public holidays as per the company’s approved Public holiday list
• Medical insurance
• Opportunity to work remotely
• Professional education budget
• Language learning budget
• Wellness budget (gym membership, sports gear and related expenses)

In Competera, we are building a place where optimal pricing decisions can be made easily. We believe that AI technologies will soon drive all challenging decisions and are capable of helping humans be better.

We are now looking for a hands-on Leader who can bridge the gap between advanced research and real-world product delivery. You will be the technical anchor for two streams: S&D (Service & Delivery), ensuring our clients get robust, profitable pricing recommendations, and R&D (Research & Development), guiding the strategy for next-gen algorithms.

What you will do

• Have clear vision and own the strategy of Data Science in the organization.
• Shape the technical roadmap based on business needs, team capacity, and long-term strategy.
• Gradually scale data science function with company growth. 
• Define the hiring profile, lead and mentor a cross-functional team of Data Scientists, and cultivate a high-performing R&D culture focused on scientific rigor and measurable business impact.
• Manage resources across both Delivery (S&D) and Research (R&D) squads.
• Act as the main bridge between R&D and S&D: translate business requirements into research tasks, and help the team adapt complex prototypes into stable, production-ready solutions.
• Architect client solutions by selecting the right models and assortment strategies to solve specific business problems using our existing stack.
• Serve as the quality gatekeeper for the team, conducting code reviews, verifying A/B test designs, and ensuring the team hits their Definition of Done (DoD).
• Drive key DS metrics such as research-to-production time, experiment velocity, and the overall quality of price recommendations.
• Communicate clearly with product managers, engineers, and occasionally clients to explain DS trade-offs, present results, and align on next steps.
• Confidently represent the company's technological expertise to investors and partners. You will clearly articulate the Data Science strategy, the core algorithms' defensibility, and the technical roadmap during high-stakes due diligence processes.
• Engage directly with customers' Data Science and Analytics teams (including PhD-level experts). You must be able to confidently explain our methodology, model assumptions, performance results, and address their most technical questions, serving as a critical differentiator during sales and pilot phases.
• Present complex model results, performance metrics, and strategic trade-offs to key stakeholders in a clear, concise, and business-focused manner.
• Support the sales and pre-sales process by building compelling technical narratives that demonstrate our value proposition and deep expertise in the pricing domain.
• Stay hands-on: dive into code and architecture when needed - whether it's designing a new experiment, fixing a critical issue in the delivery pipeline, or prototyping a new idea.

What you have

• 5+ years of experience in Data Science or a related field with a focus on delivering value to production.
• Strong Python and SQL skills, capable of writing modular and readable code for experiments and prototypes.
• Familiarity Databricks data platform and Apache Spark.
• General familiarity with Data Mesh approach and ability to leverage its best practices in collaboration with data engineers.
• Solid mathematical background, preferably in a Computer Science-related field.
• Proficiency in the scientific Python toolkit, including NumPy, pandas, scikit-learn, and either Keras/TensorFlow or PyTorch.
• Deep understanding of statistical testing methodologies, specifically A/B testing design.
• Familiarity with Time Series Forecasting approaches.
• At least 3 years of experience working with tabular and mixed (multimodal) data.
• Expertise in Causal Inference and background in Ecommerce/Retail are strong pluses.
• Upper-intermediate or higher level of English and strong public speaking skills.

Soft skills 

• You care not only about how the model works mathematically but what value it brings to the user and the business.
• Able to translate complex technical concepts into simple business language for stakeholders.
• Able to professionally and clearly communicate with PhD scientists on customer side during the sales process, pilot and rollout.
• Able to own data requirements and build efficient processes to support integration and data engineering with outlining data integration and validation requirements.
• Ready to experiment, pivot, and make data-driven decisions in a dynamic business environment.
• Proactively contribute ideas to the Product Backlog with ideas based on technical knowledge and capabilities.
• Be able to think outside of the box and find simple and elegant solutions to complex problems, including technical, product and processes-related ones.
• Curiosity and a drive to continuously learn within the domain.
• Have a truly entrepreneurial mindset.

You’re gonna love it, and here’s why:

• Rich innovative software stack, freedom to choose the best suitable technologies.
• Remote-first ideology: freedom to operate from the home office or any suitable coworking.
• Flexible working hours (we start from 8 to 11 am) and no time tracking systems on.
• Regular performance and compensation reviews.
• Recurrent 1-1s and measurable OKRs.
• In-depth onboarding with a clear success track.
• Competera covers 70% of your training/course fee.
• 20 vacation days, 15 days off, and up to one week of paid Christmas holidays.
• 20 business days of sick leave.
• We reimburse the cost of coworking.

Drive innovations with us. Be a Competerian.

We are looking for a highly skilled Senior Data Scientist to join our team.  As a Senior Data Scientist, you will be responsible for designing and implementing advanced data models, analyzing complex business problems, and developing solutions that help optimize sales, marketing, pricing, and overall commercial performance in a leading American publishing house.

The role will contribute to the transition from primarily descriptive analytics toward more advanced, data-driven decision making.

Skills & Experience

• Bachelor’s or Master’s degree in Data Science, Computer Science, Engineering, Statistics, Mathematics, or a related field;
• 5+ years of experience in data science roles;
• Strong experience applying data science and machine learning to business problems, ideally in areas such as sales, marketing, customer behavior, demand forecasting, or pricing optimization;
• Proficient in Python and SQL for data analysis, scripting, and machine learning;
• Proficient in using agentic coding tools like Claude Code, Codex, Cursor
• Experience with SciKit-learn, Pytorch, and similar machine learning frameworks for building and deploying machine learning models;
• Experience working with modern data warehouses/lakehouses like Snowflake, Databricks, BigQuery, Redshift
• Strong analytical and problem-solving skills, with the ability to translate ambiguous business problems into data-driven solutions;
• Ability to work independently and collaborate with business and technical stakeholders;
• Fluent in English and excellent communication skills.

Nice to have: 

• Strong background in data science, with extensive experience in leveraging advanced analytics and machine learning to drive data-driven business decisions.

Responsibilities

• Design, develop, and implement advanced data models and machine learning algorithms to solve complex problems in the publishing domain, like author discovery;
• Analyze sales, marketing, customer, product, and pricing data to identify opportunities and support business decisions;
• Develop data-driven approaches to areas such as demand forecasting, customer behavior, sales optimization, marketing effectiveness, and pricing;
• Work with business stakeholders to understand broad or ambiguous business challenges and translate them into analytical and machine learning solutions;
• Collaborate with cross-functional teams to understand data requirements and deliver actionable insights and recommendations;
• Build and maintain scalable data pipelines using technologies such as Snowflake, and DBT;
• Leverage Python and SQL for data analysis, model development, experimentation, and automation;
• Utilize SciKit-learn, Pytorch and similar frameworks for developing and deploying machine learning models;
• Help evolve the organization from descriptive analytics toward predictive and prescriptive analytics and data-driven decision making;
• Stay current with emerging trends and technologies in data science and machine learning.

Workload: full-time, 100% remote
 Start: ASAP
 Duration: long-term
 Format: staff augmentation
 English: strong communication skills required
 

Project Overview
Fintech project. The client needs a strong Senior Data Scientist with hands-on LLM experience who can own the technical stack end to end, work with leadership on data science strategy, and explain technical decisions clearly to non-technical stakeholders.
 

What You’ll Do
Work with leadership to shape and implement the company’s data science strategy and find opportunities to use data for business value
Own the full technical stack needed for development and deployment
Develop and maintain scalable ML models and systems
Keep data management and engineering practices solid, with efficient data pipelines
Gather requirements from cross-functional teams and provide insights
Explain complex technical concepts to non-technical stakeholders
 

Must-Have Requirements
7+ years of total experience, including participating in and/or leading complex projects
Master’s or PhD in Data Science, Computer Science, Statistics or a related field, or equivalent knowledge and experience
Hands-on expertise in modern language models and ML frameworks
Practical experience with different LLMs of various sizes and from different providers
Optimizing LLM-based pipelines for performance and cost efficiency
Building agents that work across different tools and LLMs
LLM-based data capture and extraction
Experience with modern LLM-supporting frameworks and tools
Modern LLM prompting techniques
Building evaluation pipelines using data-driven approaches
Managing feedback and training pipelines, and fine-tuning models
Python, R, or another strongly typed language
Cloud platforms (AWS, GCP or Azure) and deployment processes
Excellent communication: must confidently explain ideas and technical decisions
 

Nice to Have
Big data technologies (Spark, Hadoop, data warehouse / data lake technologies)
Data architecture and database management background
Data management and engineering tools
Knowledge of accounting and bookkeeping
Commitment to ethical AI practices and data governance
 

Key Competencies
 Problem-solving and strategic thinking, ability to innovate and drive process improvements, collaboration.
 

CV must be reworked to match the requirements above
 

Department/Project Description

GlobalLogic takes an opportunity to partner with a leading global threat intelligence platform to design and deliver an enterprise-scale, Human-in-the-Loop (HITL) AI agent platform. The initiative centers on accelerating expert analyst decision-making by augmenting high-complexity threat intelligence workflows with cutting-edge generative and agentic AI systems.


Job Description

Essential qualifications

• MSc or PhD in NLP, information retrieval or machine learning with publications, or equivalent industry research experience.
• 5+ years of applied ML, with models in production use.
• Deep experience fine-tuning transformer encoders with PyTorch and Hugging Face.
• Multi-label and imbalanced classification, probability calibration and threshold optimization.
• Learning from user feedback: learning-to-rank, active learning, bandit/exploration methods or counterfactual evaluation.
• Rigorous experiment design and statistical analysis.

Desirable qualifications

• ModernBERT or other long-context encoder models.
• Multi-stage ranking or recommender systems.
• LLM post-training (SFT, LoRA, DPO), reward or judge models, and reranker or embedding fine-tuning.
• Vertex AI Training and Pipelines.
• Experience in security, threat intelligence or fraud.

Job Responsibilities

Key responsibilities

• Fine-tune, evaluate and calibrate ModernBERT for multi-label alert classification and high-recall shortlisting.
• Design the two-stage triage cascade: model thresholds, the hand-off to the LLM, and how each stage is measured.
• Convert analyst feedback into training data through label cleaning, active learning and defined retraining criteria.
• Correct for selection bias through exploration sampling of rejected alerts and counterfactual evaluation.
• Design the dynamic few-shot strategy:
  how decision points are represented
  how examples are selected
  how conflicting or stale human decisions are handled.
• Define the feedback and reward schema with the Lead Agent Engineer.
• Set offline and online evaluation, and review retrained models before production promotion.

We’re looking for a Senior Data Scientist who can take a data problem from an initial business question to a solution that works in production.

This role is not only about building models. We’re looking for someone who is comfortable exploring imperfect data, challenging assumptions, choosing the right approach for the problem, and working closely with engineers and product teams to turn experiments into reliable solutions.

What you’ll work on:

• Explore and analyze complex datasets to understand patterns, dependencies, and opportunities.
• Turn business and product questions into well-defined data science problems.
• Design, train, evaluate, and improve machine learning and statistical models.
• Work on the full ML lifecycle: data exploration, feature engineering, experimentation, validation, deployment, and monitoring.
• Decide when an ML model is actually needed — and when a simpler statistical or analytical approach will do the job better.
• Define meaningful metrics and design experiments to measure the impact of proposed solutions.
• Work with Data and ML Engineers to bring models into production and keep them reliable over time.
• Investigate model performance, data quality issues, drift, and unexpected production behavior.
• Present findings and recommendations in a way that is useful to both technical teams and business stakeholders.
• Help shape our data science practices through code reviews, technical discussions, and mentoring.

What we’re looking for:

• 5+ years of hands-on experience in Data Science or Machine Learning.
• Strong Python skills and practical experience with Pandas, NumPy, scikit-learn, or similar tools.
• Strong foundation in statistics, probability, and machine learning.
• Experience working with real-world datasets where the data is not always clean, complete, or perfectly structured.
• Confident SQL skills.
• Experience building ML solutions beyond notebooks and prototypes.
• Good understanding of model validation, experimentation, feature engineering, and performance evaluation.
• Experience with production ML workflows and familiarity with MLOps practices.
• Experience with AWS, GCP, or Azure.
• Ability to explain technical decisions, trade-offs, and results clearly.
• English — Upper-Intermediate or higher.

It would be a plus if you have:

• Hands-on experience with LLMs / Generative AI, NLP, recommendation systems, forecasting, or computer vision.
• Experience with PyTorch or TensorFlow.
• Experience with tools such as Spark, Databricks, Snowflake, MLflow, or Airflow.
• Experience designing A/B tests and working with product experimentation.
• Experience mentoring other Data Scientists or taking ownership of technical decisions.