Jobs Data Scientist

Працюємо над задачами комп’ютерного зору для безпілотних систем. Основний фокус — донаведення: детекція, трекінг і пріоритизація цілей у реальному часі з обмеженими ресурсами.
 

Що будете робити

• Розробляти та адаптувати CV/ML моделі (детекція, сегментація, трекінг)
• Проєктувати пайплайни та ансамблі з урахуванням latency, FPS, пам’яті та енергоспоживання
• Деплоїти та оптимізувати інференс на embedded-пристроях (Jetson / Raspberry Pi / ARM64)
• Працювати над стабільним real-time трекінгом (відеостабілізація, калібрування камер, robustness)
• Інтегрувати ML/CV компоненти у продакшн-системи (Linux)
• Співпрацювати з embedded-інженерами над low-latency відеопайплайнами
 

Що важливо

• 3+ років досвіду в Computer Vision / Machine Learning
• Python або C++ (бажано обидва)
• Практичний досвід з OpenCV та PyTorch / TensorFlow
• Досвід із сучасними моделями (YOLO, MobileNet, U-Net або аналоги)
• Глибоке розуміння object tracking (вибір під задачу, тюнінг, стабільність, recovery)
• Досвід роботи з Linux, Git, Docker
 

Буде плюсом

• Деплой та оптимізація під embedded (Jetson / ARM64)
• ONNX / TensorRT / OpenVINO / NCNN
• Конвертація та оптимізація моделей під NPU / edge
• Досвід з ROS1 / ROS2, Gazebo
 

Чому це цікаво
• Робота з обмеженими ресурсами та складними умовами
• Вплив на архітектуру пайплайнів і моделей
• Швидкий цикл експериментів і перевірки в реальному середовищі

Твій старт із PwC Lviv SDC: Вакансія, яка відкриває двері у майбутнє

PwC — це глобальна мережа із більш, ніж 370 000 професіоналів у 149 країнах, яка перетворює виклики на можливості. Ми створюємо інноваційні рішення в аудиті, консалтингу, податках і технологіях, об'єднуючи знання з усього світу.

PwC SDC Lviv, відкритий у 2018 році, є частиною цього глобального простору. Це місце, де технології поєднуються з командним духом, а амбітні ідеї знаходять своє втілення в реальних проєктах для Центральної та Східної Європи.

Чому це твоє?

PwC SDC Lviv — це не просто робота. Це можливість:

• Реально відчути, що таке працювати в міжнародній компанії.
• Освоїти практичні навички, які будуть актуальними у світі, що постійно змінюється.
• Бути частиною команди, яка цінує твої ідеї та підтримує твій ріст.

Що ми гарантуємо?

• Формат роботи: Дистанційно або в комфортному офісі у Львові — ти обираєш сам.
• Розвиток: Особистісний план розвитку, менторинг, курси англійської та польської мов.
• Стабільність: Офіційне працевлаштування з першого дня, щорічний перегляд зарплати та кар'єрних перспектив.
• Корпоративна культура: Події, що об'єднують команду, і простір, де кожен може бути собою.

Що на тебе чекає?

Наша команда допомагає провідним фінансовим установам Польщі та інших країн вирішувати складні бізнес-завдання у сфері управління ризиками, аналітики даних та сучасних технологій.

Ми працюємо над проєктами у сферах кредитного та ринкового ризику, IFRS 9, стрес-тестування, ICAAP, розробки та валідації моделей, регуляторного комплаєнсу та цифрової трансформації функцій ризик-менеджменту.

Ми шукаємо студентів та випускників, які хочуть розпочати кар'єру у сфері кількісного аналізу, фінансового моделювання та Data Science.

Ваші майбутні завдання:

• підтримка проєктних команд у розробці аналітичних та кількісних рішень для банків і фінансових установ;
• участь у проєктах з кредитного ризику (IRB), IFRS 9, стрес-тестування, ICAAP, валідації моделей та трансформації ризик-менеджменту;
• аналіз та обробка великих масивів даних;
• вивчення процесів розробки, тестування, впровадження та моніторингу моделей;
• застосування статистичних методів та алгоритмів машинного навчання для вирішення бізнес-завдань;
• підготовка аналітичних звітів, презентацій та рекомендацій для клієнтів;
• робота з Python, R, SQL, SAS, Git та хмарними технологіями;
• знайомство з AI та GenAI-рішеннями, які використовуються у фінансовому секторі;
• розуміння того, як математичні моделі та регуляторні вимоги застосовуються на практиці в банківській сфері.

Вимоги:

• студент або випускник напряму: математика; статистика; економетрика; фізика; Data Science; фінансова математика; комп’ютерні науки та суміжні спеціальності;
• англійська мова на рівні B2 або вище;
• інтерес до фінансових ризиків, аналітики даних та кількісного моделювання;
• базові знання статистики, теорії ймовірностей, економетрики, математичного моделювання або машинного навчання;
• знання хоча б однієї з мов програмування: Python, R, SQL або SAS;
• зацікавленість у застосуванні AI та GenAI у фінансовому секторі;
• аналітичне мислення, уважність до деталей та навички вирішення проблем;

Чому саме PwC?

Це твій шанс не просто працювати, а вести команду й зростати разом із нею. Ми створюємо середовище, де твоя експертиза та лідерський потенціал справді важливі.

Готовий до виклику? Надсилай своє резюме і приєднуйся до команди, яка формує майбутнє!

Політика у сфері конфіденційності та персональних даних:

https://www.pwc.com/ua/uk/about/privacy.html

About the Role
We are seeking an AI & Data Science Consultant to drive the discovery, strategy, and end-to-end delivery of advanced AI initiatives for EPAM’s global clients. This role is a unique hybrid, blending strategic business consulting with deep practical engineering. You will collaborate directly with client executives and EPAM’s delivery teams to transform complex business challenges into scalable, production-ready AI, Machine Learning, and Agentic solutions.
 

Key Responsibilities

• Consult & Strategize: Lead client-facing discovery sessions and workshops to identify high-value AI opportunities, define MVP scopes, and map out strategic delivery roadmaps.
• Design & Architect: Shape scalable AI products incorporating Generative AI, RAG, multi-agent frameworks, semantic layers, and advanced analytics.
• Bridge the Collaboration: Serve as the central link connecting business stakeholders, data scientists, ML engineers, DevOps, and product teams.
• Drive Delivery & MLOps: Apply a strong engineering mindset to ensure solutions are secure, production-ready, and smoothly integrated into cloud infrastructures.
• Support Business Growth: Partner on pre-sales activities, solution estimation, and proposal development while contributing to EPAM’s internal AI and MLOps offerings.
   

Requirements

• Professional Experience: 3+ years of hands-on experience in Data Science, ML, or Applied AI, with a proven track record of bringing models from concept to production.
• Consulting & Communication: Strong active listening, presentation, and storytelling skills; ability to confidently translate complex AI concepts for C-level stakeholders.
• AI & Agentic Tech Stack: Experience with RAG, LLM applications, multi-agent orchestration, and tools like LangChain, LangGraph, CrewAI, LlamaIndex, or Vector DBs.
• Data & Architecture: Understanding of Semantic Layers, metadata, knowledge graphs, and data platforms (such as Databricks or Snowflake).
• Engineering & MLOps: Familiarity with the production model lifecycle, MLOps/LLMOps pipelines, and cloud platform integrations.
   

Nice to Have

• Experience mentoring cross-functional AI teams or driving enterprise-scale GenAI adoption.
• Practical knowledge of cloud infrastructure and DevOps tooling.
• Insights into AI governance and ethical AI frameworks.

Необхідний досвід з навичками

Стабільна FinTech компанія з перспективними планами розвитку та зростання у пошуку Head of Data Science / Head of Credit Risk Analytics.

Продукт — онлайн-сервіс для кредитування, який продовжує доопрацьовуватись новими фічами.

Роль відповідає за побудову, розвиток та ефективність системи прийняття кредитних рішень, скорингових моделей і операційне управління кредитним ризиком. Забезпечує якість кредитного портфеля через розвиток моделей, аналітики та автоматизацію процесів прийняття рішень.

Обов'язки:

• Побудова, розвиток та підтримка автоматизованої системи прийняття кредитних рішень (Decision Engine);
• Розробка, імплементація, моніторинг та оптимізація скорингових моделей із використанням Machine Learning;
• Розробка, тестування та оптимізація Risk Rules, cut-off та лімітних політик;
• Аналіз кредитного портфеля, рівня дефолтів, ефективності скорингових моделей та ключових ризик-показників;
• Проведення Champion/Challenger та A/B тестування для оцінки ефективності моделей;
• Розробка та автоматизація аналітичної звітності для моніторингу кредитного ризику;
• Управління якістю даних та розвиток Data Warehouse;
• Інтеграція, оцінка та моніторинг ефективності зовнішніх джерел даних;
• Постановка технічних завдань команді розробки, тестування та контроль впровадження змін;
• Формування рекомендацій щодо оптимізації кредитної політики, підвищення якості портфеля та покращення бізнес-результатів;

• Взаємодія з Chief Risk Officer щодо впровадження затверджених ризик-політик у систему прийняття кредитних рішень.

   

Вимоги:

Обов'язкові:

• Досвід побудови та розвитку скорингових моделей;
• Досвід роботи у fintech, МФО або банківському секторі;
• Впевнене володіння Python;
• SQL (PostgreSQL, MS SQL);
• Практичний досвід використання Machine Learning;
• Досвід аналізу кредитного портфеля та управління кредитним ризиком;
• Знання статистики та методів аналізу даних;
• Досвід роботи з Decision Engine;
• Досвід розробки та підтримки Risk Rules;

• Досвід побудови аналітичної звітності та роботи з великими масивами даних.

   

Буде перевагою:

• Deep Learning;
• Excel Advanced;
• Power BI або Tableau;
• MLOps;
• Досвід роботи з альтернативними джерелами даних та antifraud-рішеннями.

Досвід роботи в мікрокредитній установі або банку – конкурентна перевага.

Who We Are

At Bennett Data Science, we've been pioneering the use of predictive analytics and data science for over a decade for some of the biggest brands and retailers. We're at the top of our field because we focus on delivering actionable AI for our clients. Our deep experience and product-first attitude set us apart from other groups and gets us the business results our clients want.

Why You Should Work With Us

You'll be exposed to a wide range of clients who are at the cutting edge of innovation in their field and get to work on fascinating problems, supporting real products, with real data. We help lots of companies, from some of the largest companies in the world to small startups in Silicon Valley who are building the next big thing.

Expert Mentorship: Direct guidance from senior staff with 20+ years of applied ML experience

Competitive Compensation: Market-rate pay with performance upside

Fully Remote: Work from any location of your choice, on a flexible schedule

The Role:
We are seeking a Senior Data Scientist to support the design, development, validation, and productionization of an advanced forecasting solution. This is a hands-on role requiring strong statistical judgment, practical modeling experience, and the ability to translate analytical findings into clear recommendations.

The successful candidate will work closely with data science, data engineering, and business stakeholders throughout the full modeling lifecycle, from exploratory analysis and model development to deployment, documentation, and handoff.

Key Responsibilities

• Develop, evaluate, and improve forecasting models using Python.
• Establish appropriate baselines and compare alternative modeling approaches.
• Design rigorous time-based backtesting and model-validation frameworks.
• Develop methods for handling limited historical data, changing patterns, and structural shifts.
• Produce prediction intervals and other measures of forecast uncertainty.
• Identify, test, and monitor potential forecast drivers.
• Develop configurable scenario-planning and sensitivity-analysis capabilities.
• Create model diagnostics and early-warning indicators.
• Write modular, tested, maintainable, and well-documented code.
• Collaborate with data engineering on production pipelines, model tracking, and automated forecast runs.
• Explain model results, assumptions, limitations, and changes to technical and non-technical stakeholders.
• Support technical documentation, knowledge transfer, and final solution handoff.
  
  Required Qualifications
• Five or more years of professional experience in data science, applied statistics, econometrics, forecasting, or a related field.
• Strong practical experience developing forecasting or predictive-modeling solutions.
• Advanced Python and SQL skills.
• Strong knowledge of regression, time-series methods, feature engineering, residual analysis, and uncertainty estimation.
• Experience designing rolling or time-based backtests and preventing data leakage.
• Ability to distinguish meaningful predictive signals from unstable or spurious correlations.
• Experience developing production-quality analytical code using version control and standard software-development practices.
• Strong analytical communication and stakeholder-management skills.

• Ability to work independently and deliver within a defined project timeline.
   

  Preferred Qualifications

• Experience developing scenario-planning or decision-support tools.
• Experience working with small historical samples or significant changes in underlying data patterns.
• Familiarity with Microsoft Fabric, Azure, MLflow, or comparable cloud-based analytics platforms.
• Experience productionizing forecasting models and implementing ongoing performance monitoring.
• Experience presenting analytical findings to Finance or senior leadership.

We are looking for a Quantitative Analyst to become the mathematical backbone of our Trading Core and Risk Tech squads. In a high-frequency trading environment processing billions in volume, success is defined by precision. 

In this role, you will design, backtest, and optimize the core mathematical models that drive our pricing engines, automated market-making algorithms, risk management frameworks (A/B/C-book optimization), and liquidation mechanics. You will sit at the intersection of advanced mathematics, data science, and high-performance software engineering, working directly with Core Product Managers and engineering teams to turn complex financial data into proprietary algorithmic 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

• 3+ years of experience as a Quantitative Analyst / Researcher or Data Science
• 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)

🔹Who we are!

At Levi9, we are passionate about what we do. We love our work and together in a team, we are smarter and stronger. We are looking for skilled team players who make change happen. Are you one of these players?
 

🔹About the project

Our client is a leading media company in Western Europe, delivering high-quality content across newspapers, magazines, radio, TV, websites, apps, and podcasts. Their brands reach millions of people every day and help shape the media landscape through independent and trusted journalism.

We are developing AI capabilities for the digital advertising domain, helping improve campaign management, analytics, operational efficiency, and user engagement. The team will create a range of AI-driven solutions that address real business needs and evolve alongside the platform and market.

These solutions may combine agentic workflows, large language models, enterprise data, and backend integrations. They are designed with security, scalability, automated evaluation, observability, and cost efficiency in mind and are deployed as modular services on AWS.
 

🔹Responsibilities
— Develop data products at the intersection of data science, machine learning engineering, and AI to enable Total Video at scale
— Provide the crucial holistic data focus needed to successfully integrate the worlds of TV advertising and online video advertising
— With your expertise, you translate complex business challenges into concrete data solutions and contribute directly to the further development of an innovative product, that will transform the advertising market
— Analyze complex datasets and translating stakeholder challenges into concrete solutions, that you continuously improve iteratively
— Develop and implement scalable data products and models within a production environment
— Collaborate within a dedicated development and product team
 

🔹Requirements
— 5+ years of experience in a similar role
— Deep expertise in data science and machine learning, enabling you to design, train, and implement models in production environments
— Experience transforming data models into scalable data products, preferably within a media or advertising environment
— Strong analytical and conceptual skills, allowing you to recognize patterns in complex datasets and translate them into concrete solutions
— Experience with machine learning tools and MLOps systems such as Docker, Databricks, and MLflow
— Strong communication skills to clearly convey insights to both technical and non-technical stakeholders
 

🔹9 reasons to join us:

1. Today we’re working with the technology of tomorrow.
2. We don’t wait for a change. We are the change.
3. We’re experts in creating experts (Levi9 academy, Lead9 program for leaders).
4. No micromanagement. We are free birds with a clear understanding of what the high performance is!
5. Learning in Levi9 never stops (unlimited Udemy for business, meetups, English courses, Professional trainings).
6. Here you can train your body and mind.
7. We’ve gathered the best locations — comfortable, cozy and pet-friendly offices in Kyiv (5 minutes from Olimpiyska subway station) and Lviv (5 minutes from the Opera Theatre) with regular offline internal events
8. We have master’s degree in work-life balance.
9. We are actively supporting Ukraine with constant donations and volunteering

We are looking for a Computer Vision / C++ Engineer to join our team and work on real-world computer vision systems.

Responsibilities:
 

- Develop and maintain computer vision solutions in C++;

- Work with geometric computer vision (projective and epipolar geometry);

- Implement classical CV algorithms (demosaicing, tracking, optical flow, color calibration, image enhancement);

- Fine-tune existing deep learning models (e.g. YOLO) and train custom models using PyTorch;

- Deploy DL models in C++ using inference runtimes (ONNX Runtime, OpenCV DNN or similar);

- Implement model output decoding and post-processing;

- Develop camera-related software;

- Write efficient multi-threaded code.

Requirements:
 

- Strong C++ skills;

- Solid background in computer vision;

- Practical experience with deep learning for CV and PyTorch;

- Experience with deploying models to C++ applications and inference runtimes;

-Experience with multi-threaded programming and camera systems.

We're currently working on a Senior KDB Developer opportunity in Kraków (hybrid, B2B contract) — a chance to build core components of a Global FX MI & Analytics platform, working closely with Quant Analysts and the Electronic Trading Technology team on tick data feeds and scalable analytics solutions.

Location: Kraków, Poland (Hybrid – 2-3 days onsite per week)
B2B Contract

Key Responsibilities

• Develop key components of the Global FX MI & Analytics platform.
• Collaborate closely with Quantitative Analysts and the Electronic Trading Technology team to deliver fit-for-purpose solutions.
• Contribute to the design and architecture of tick data access feeds and scalable analytics solutions.
• Implement API-based functionality and integrations.
• Enhance and expand common tools, components, and APIs.
• Drive automation across metrics, builds, testing, and deployment processes.

Required Skills & Experience

• Strong experience with Test-Driven Development (TDD) and Behaviour-Driven Development (BDD), with a focus on specification-based testing.
• Excellent analytical and problem-solving skills, including knowledge of market data, trade data, and fundamental statistical concepts.
• Proven ability to query, analyse, and work with large volumes of data.
• Strong communication skills and a collaborative team-oriented approach.
• Deep understanding of the q programming language and experience writing high-performance code.
• Solid knowledge of KDB+ tick architecture.
• Ability to thrive in a fast-paced and dynamic environment.
• Appreciates diverse perspectives and leverages them to enhance decision-making and team outcomes.
• Exposure to the full Software Development Lifecycle (SDLC), including build automation, deployment, and support.
• Experience with a second programming language (object-oriented or functional) would be highly advantageous.

Most AI engineers build demos. You'll build the production system a client's business actually depends on.

Team: AI Delivery Practice 

Location: Remote (Ukraine/Europe) 

Employment: Full-time 

Reports to: Head of AI Delivery/Engineering, not Sales, not Customer Success

Travel: Typically 20–30%, concentrated around engagement kickoffs and go-live weeks, confirmed with you per engagement, not a surprise

The Job, Honestly

Most AI engineering jobs let you perfect a demo. This one doesn't. You get one client, one real opportunity, and six to twelve weeks to turn it into something running on live traffic — in their environment, on their data, with their security team watching. Then you do it again, for a different client, in a different industry, with a different set of constraints.

Success here isn't measured in closed tickets. It's measured in production adoption, business impact, and whether the client can run what you built without you standing over their shoulder.

If that sounds like the fun part of the job rather than the exhausting part, keep reading.

The AI Practice

You won't be the only person figuring this out. Delivery patterns each engineer builds — a guardrail design, an eval harness, a way of scoping a RAG project so it doesn't die in week 6 — get written down and reused by the next person on the next engagement. You're not starting from zero each time, and neither is the person after you.

Engagements span the kind of variety a single-product company can't offer: a RAG system on a client's live data one quarter, an agent with real write-access and real guardrails the next, a legacy enterprise stack under regulatory constraints after that.

What the Work Actually Looks Like

Discovery through production — not discovery through a slide deck

• You sit with the client's team — their repos, their data, their stand-ups — and find the AI opportunity worth betting six weeks on, not the one that looks best in a pitch

• You analyze their business processes, data landscape, and existing systems well enough to translate a business problem into a production-ready AI architecture

• You prototype it, validate it with real end users, and iterate on measurable outcomes before you've convinced yourself it's right

• You ship it — and you're still the one on call when it breaks in week two of production, not someone who inherits your code

• You capture what you learned so the next engagement, and the next engineer, start a little further ahead than you did

Before the contract is even signed

• Sales brings you into scoping calls, because "can this actually ship in 6–12 weeks" is an engineering judgment, not a sales one — and what you say shapes what gets sold

The actual engineering, once you're in

• Production LLM applications — agents, RAG, orchestration — built on whatever stack the client's environment actually runs on, not the stack that's most fun to write in

• Integrations into whatever the client already has: their APIs, identity provider, cloud, messaging systems, the legacy piece nobody wants to touch

• Reliability work that never shows up in a demo: tool-call guardrails, retries, fallbacks, human-in-the-loop checkpoints

• Eval pipelines, golden datasets, and observability — because the job isn't done at go-live, it's done when the client can run the thing on their own

• The unglamorous optimization work: latency, cost, security, maintainability — whatever the client's actual bottleneck turns out to be

Who Tends to Thrive Here

Less a checklist, more a description of the person who's already done a version of this job:

• You can talk in detail about a production AI system you shipped for an external customer — what they wanted at kickoff, what they wanted at launch, and why that changed. This matters more to us than any line on your CV

• 5+ years as a commercial software engineer, with strong fundamentals and the ability to pick up whatever stack a given engagement requires — we hire for engineering depth and adaptability, not a fixed programming language

• Fluent with at least one modern LLM platform (OpenAI, Anthropic, Gemini, Azure OpenAI, Bedrock), and comfortable in the RAG/vector/embeddings/context-engineering space without needing a primer

• You've built agents with something like LangGraph, LangChain, Semantic Kernel, CrewAI, or AutoGen — and you've watched one misbehave in production, which taught you more than the framework docs did

• You can ship and run things on AWS/Azure/GCP with Docker, Kubernetes, and CI/CD — and you understand enough about IAM, auth, and networking to not be the reason the client's security team says no

• You can hold your own in a room with a skeptical enterprise stakeholder who's been burned by an AI vendor before — not by having all the answers, but by being straight about trade-offs

• Upper-Intermediate English or better, because a lot of this job is a conversation, not a pull request

Extra credit, not a requirement

• You've built eval frameworks, golden datasets, or regression tests for AI systems, or worked with LangSmith, Langfuse, Arize Phoenix, or MLflow

• You know your way around AI governance, guardrails, hallucination mitigation, or prompt injection — or MCP / A2A and where AI interoperability is heading

• You've touched Databricks, Azure AI Foundry, Bedrock, or Vertex AI at the platform level, not just the API level

• You've done consulting, solution architecture, or technical pre-sales before — or worked inside a regulated industry (healthcare, finance, telecom, government) and know why that changes everything

• You have a GitHub history, a conference talk, or a client-co-authored case study that shows this rather than just claiming it

Your First 90 Days

• Weeks 1–2: Embedded in your first client's environment, with a validated, highest-value use case identified jointly with their team — not handed to you

• Weeks 3–8: Architecture designed, prototype validated with real end users, production build underway

• Weeks 9–12: System live on real traffic, eval pipeline in place, documentation and handover ready for the client's own team to take over

What We Offer

• Competitive compensation with bonuses tied to successful production go-lives; B2B contract available

• Direct visibility into your impact — your code runs at the client, not sitting in a backlog

• No two engagements are the same: a fintech RAG project this quarter, a regulated-industry agent build next

• Certifications (Anthropic, OpenAI, AWS, GCP, Azure) and a professional development budget

• Health insurance, English classes, sports activities, and a culture that tells you the hard parts of the job up front — like we just did

If the six-to-twelve-week cycle, the client-hopping, and the "you're still holding the pager" ownership sound energizing rather than draining — this is probably your role. Send us your CV, and tell us about the production AI system you're proudest of shipping.

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

We are seeking a motivated Middle Data Scientist to join our data science team. The ideal candidate has solid experience with supervised machine learning, strong analytical skills, and is comfortable working with real-world, large-scale data. In this role, you will contribute to building, improving, and maintaining machine learning models that support our advertising products and business decisions.

This position is based in Ukraine. The team primarily works remotely, with occasional in-person meetings in the Kyiv or Lviv office.

Key Responsibilities:

• Develop and maintain supervised machine learning models to support ad targeting, measurement, and campaign optimization.
• Perform data analysis, data validation, and feature engineering on large and complex datasets.
• Collaborate with product, engineering, and analytics teams to translate business needs into data science solutions.
• Evaluate model performance using appropriate metrics and iterate on model improvements.
• Support optimization and experimentation efforts related to advertising performance.
• Assist with deploying models to production in collaboration with engineering teams.
• Monitor model performance and help troubleshoot issues in production.
• Communicate results, insights, and limitations to both technical and non-technical stakeholders.
• Stay up to date with industry best practices in data science and machine learning.

Qualifications:

Education:

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

Experience:

• 2–4 years of experience in data science, machine learning, or a related analytical role.
• Hands-on experience applying machine learning to real business problems.

Technical Skills:

• Proficiency in Python.
• Solid experience with SQL and relational databases.
• Experience with machine learning libraries such as scikit-learn. Experience with TensorFlow or PyTorch is a plus.
• Good understanding of supervised learning techniques such as linear regression, logistic regression, and tree-based models including decision trees, random forests, and gradient boosting.
• Experience with data manipulation and analysis using pandas and NumPy.
• Familiarity with model evaluation techniques and performance metrics.
• Basic understanding of working with production systems or machine learning pipelines is a plus.

Other Skills:

• Strong analytical and problem-solving abilities.
• Ability to work independently while collaborating within a team.
• Clear communication skills and ability to explain technical concepts simply.
• Strong organizational skills and attention to detail.
• Comfortable communicating with U.S.-based teams.

About RiskSeal

RiskSeal is a B2B startup serving fintech clients globally. Our API-first credit risk management platform enables digital banks and consumer lenders to simplify onboarding through data-driven insights.

We are growing rapidly and value collaboration with passionate, creative professionals. Join us to innovate, contribute your expertise, and grow alongside our team.

Headquarters: Dallas, Texas, USA

European Office: Warsaw, Poland

Job Description

We are looking for a motivated Data Scientist to join our team in a fully remote role. The ideal candidate has experience in credit risk modelling, strong Python and SQL skills, as well as hands-on experience with scoring models. You will build and optimize advanced credit risk and anti-fraud models by leveraging machine learning, anomaly detection, and alternative data alongside traditional scoring. Working with client data, you will translate complex algorithmic outputs into clear, actionable insights for clients, partners, and internal teams.

What we're looking for:

• 2+ years of experience in credit risk modelling.
• Solid knowledge of classical methods (logistic regression, decision trees, scorecards) and ML (boosting, random forests, neural networks).
• Strong Python and SQL skills.
• Background in a bank, fintech, or data vendor environment.
• Experience with PD modelling, scoring, and portfolio performance metrics.
• Comfortable working end-to-end: from raw data to a validated, production-ready output.
• Able to present findings clearly to non-technical audiences, including clients and business partners.
• Upper-Intermediate English level or higher.

What you'll be doing:

• Build, test, validate, and implement predictive credit risk, scoring, and anti-fraud models.
• Analyze complex client data and alternative data streams to uncover hidden insights and deliver data-driven recommendations that guide strategic decisions.
• Continuously monitor and audit model performance (tracking drift, Gini, and PSI) to ensure long-term stability and accuracy.
• Design and experiment with new data attributes, leveraging digital footprint and transactional data to improve model predictability.
• Present analytical findings, model performance reports, and innovative ideas directly to clients, partners, and internal teams.
• Conceptualize new analytical products, scorecards, and advanced data solutions to expand RiskSeal's product offering.
• Partner closely with business, product, and tech teams to deploy models and seamlessly turn data insights into scalable, real-world value.

Nice to Have

• Experience working with alternative data sources.
• Knowledge of model governance and validation frameworks.
• Experience presenting analytical solutions directly to clients.
• Exposure to fintech products, digital lending, or embedded finance.

What We Offer

● A fully remote, full-time role in a growing product fintech company
● A fast-paced environment with minimal bureaucracy and quick decision-making
● The opportunity to work on credit risk models, scoring solutions, and real client data
● Direct collaboration with business, product, and tech teams
● The chance to contribute to analytical products that create real value for clients
● Competitive compensation and opportunities for professional growth