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Machine Learning Data Analyst
Высокий балл обусловлен статусом компании (Unicorn), работой с передовыми технологиями ИИ и возможностью влиять на глобальный продукт. Гибкий график и культура высокой ответственности делают вакансию привлекательной для амбициозных специалистов.
Сложность вакансии
Роль требует сочетания навыков классического дата-аналитика и инженера данных, включая уверенное владение Python, SQL и инструментами оркестрации (Airflow). Высокая планка ответственности в компании-единороге и работа с ML-пайплайнами повышают сложность позиции.
Анализ зарплаты
Зарплата для данной позиции в Сербии не указана, но рынок для опытных Data-специалистов в международных продуктовых компаниях (особенно уровня Unicorn) обычно предлагает условия выше среднего по региону. Указанные рыночные оценки отражают уровень Middle/Senior специалистов в Белграде.
Сопроводительное письмо
I am writing to express my strong interest in the Machine Learning Data Analyst position at Incode. With over three years of experience in building robust data pipelines and a solid background in engineering, I am excited about the opportunity to contribute to your ID Document Intelligence team. My expertise in Python and SQL, combined with hands-on experience in workflow orchestration using tools like Airflow, aligns perfectly with your requirements for automating ML data workflows.
In my previous roles, I have focused on ensuring data quality and reliability, which I understand is crucial for Incode’s mission to power a world of digital trust. I am particularly drawn to Incode's status as a Series B unicorn and your commitment to ethical AI. I am confident that my technical skills in AWS Redshift and my passion for MLOps will allow me to make a significant impact on your document intelligence systems and help scale your data infrastructure to meet the needs of millions of users worldwide.
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Описание вакансии
POWER A WORLD OF TRUST
Incode is the leading provider of world-class identity solutions that is reinventing the way humans authenticate and verify their identities online to power a world of digital trust.
Through our revolutionary identity solutions, we are unleashing the business potential of universal industries including finance, government, retail, hospitality, gaming, and more, by reducing fraud and transforming human interactions with data, products, and services.
We’re in the process of rapidly scaling our diverse global team and we’re looking for entrepreneurial individuals and leaders who are curious, driven, and excited by ownership to join a Unicorn-status scale-up!
About Incode
Incode is a Series B unicorn rewriting how the world proves identity. Our AI-powered platform lets leading banks, fintechs, marketplaces, and governments deliver friction-free experiences while defeating fraud and safeguarding privacy. Customers such as Citi, AirBnB, Block, Chime, Sixt, and TikTok rely on Incode to power their identity verification and security.
Recently named a Leader in the Gartner® Magic Quadrant™ for Identity Verification, we’re scaling fast - and we’re looking for data professionals passionate about building the pipelines and systems that power world-class ML models.
The Impact You’ll Make
As a Data Analyst on the ID Document Intelligence team, you’ll design and maintain the data pipelines that fuel Incode’s machine learning ecosystem. You’ll ensure that data flows efficiently and accurately through every stage of model training, labeling, and performance tracking.
Your work will be essential to maintaining the scalability, quality, and precision of Incode’s document intelligence systems used by millions worldwide.
What You’ll Own & Drive
- Design, build, and maintain automated data pipelines for collection, labeling, validation, and metric computation that support ML training and evaluation.
- Establish and monitor data and labeling quality standards - drive consistency checks, accuracy audits, and root-cause analysis when issues impact model outcomes.
- Define, implement, and automate model evaluation metrics and reporting that reflect real-world product use cases and business goals.
- Build scalable systems for performance tracking, dashboards, and monitoring to enable fast, data-driven decisions across teams.
- Develop and operate reliable workflow orchestration (Airflow, Prefect, or similar) to schedule, observe, and troubleshoot end-to-end pipelines.
- Write clean, maintainable Python code and performant SQL to process large datasets, leveraging AWS Redshift (and related AWS tooling) where needed.
- Partner closely with ML engineers, analysts, and product stakeholders to prioritize work by impact, unblock execution, and continuously improve internal tooling for analysis and evaluation.
Your Background
- 3+ years of experience as a Data Analyst or in a similar data infrastructure role.
- Strong Python programming skills with focus on clean, maintainable code.
- Solid SQL expertise and experience with cloud or columnar databases (e.g., AWS Redshift).
- Hands-on experience with workflow orchestration tools (Airflow, Prefect, Dagster, etc.).
- Proven experience in data quality management, data preparation, or ML data pipelines.
- Understanding of metric computation, data labeling, and automation in ML workflows.
- Strong collaboration and problem-solving skills.
- Background in mathematics, physics, or engineering.
Preferred Experience
- Familiarity with big data technologies or data infrastructure optimization.
- Experience with labeling workflows or ML data preparation pipelines.
- Exposure to AWS or other cloud-based data solutions.
- Interest in machine learning operations (MLOps) and scalable ML systems.
The Qualities That Set You Apart
- A builder’s mindset - driven to create efficient, automated systems.
- Strong ownership of data quality and reliability.
- Ability to balance technical depth with practical execution.
- Collaborative, curious, and eager to improve infrastructure continuously.
- Passion for empowering machine learning systems with clean, structured data.
Why Incode?
Mission with Meaning - Build systems that enable ethical, seamless identity verification for millions.
Rocket-Ship Growth - Join a company scaling globally with AI at its core.
Elite Team & Technology - Collaborate with top engineers and data scientists redefining document intelligence.
Ownership & Autonomy - Operate with end-to-end responsibility for impactful data pipelines.
Global Impact - Your work will power real-world AI experiences trusted by major enterprises.
Aspects of our Culture:
- High performance
- Freedom & responsibility
- Context, not control
- Highly aligned, loosely coupled
- Continuous Feedback
- Promotions & Development
- Learn more about Life at Incode!
Benefits & Perks:
- Flexible Working Hours & Workplace
- Open Vacation Policy
Equal Opportunities:
Incode is an equal opportunity employer, committed to creating a diverse and inclusive work environment. We take great pride in having an inclusive, diverse, and global team, and we are always looking for talented and passionate individuals from all backgrounds and walks of life. As part of our commitment to inclusion, we ensure that reasonable accommodations are available throughout the hiring process. If you require any accommodation due to a disability or specific need, please let our Talent Acquisition team know—we’ll do our best to support you.
Applicant Data Privacy:
We will only use your personal information concerning Incode’s application, recruitment, and hiring processes.
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Навыки
- Python
- SQL
- AWS Redshift
- Airflow
- Prefect
- Dagster
- Data Pipelines
- Machine Learning
- MLOps
- Data Quality
- Data Labeling
Возможные вопросы на собеседовании
Проверка опыта работы с инструментами автоматизации, указанными в вакансии.
Расскажите о самом сложном пайплайне, который вы настраивали в Airflow или Prefect. С какими проблемами вы столкнулись?
Вакансия делает упор на качество данных для обучения моделей.
Как вы организуете процесс проверки качества данных (data quality) перед тем, как они попадут в модель машинного обучения?
Работа предполагает взаимодействие с AWS Redshift и большими данными.
Какие методы оптимизации SQL-запросов вы используете при работе с колоночными базами данных, такими как Redshift?
Позиция требует понимания жизненного цикла ML.
Как вы определяете метрики успеха для процесса разметки данных и как автоматизируете их отслеживание?
Оценка навыков командного взаимодействия и приоритизации.
Как вы подходите к приоритизации задач, когда требования от ML-инженеров и продуктовых менеджеров конфликтуют между собой?
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