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Warszawa, województwo mazowieckie, Polska
Onsite
This role function is to help the organization scale and standardize how analytical and AI capabilities are developed, deployed, and maintained. The ideal candidate not only builds high-quality models but takes ownership of ensuring those models move into active use and continue delivering value over time. They are passionate about turning one-time analyses into repeatable, organization-wide solutions and developing tools and applications that put data-driven capabilities directly in the hands of business users. They partner closely with BI, IT, and business stakeholders to embed data-driven decision-making durably into day-to-day operations.
What You Will Do:
Develop Predictive Models: Designs and implement statistical models and machine learning algorithms to analyze complex datasets and generate actionable insights.
Data Processing and Management: Collects, cleanses, and organizes large-scale data from various sources to ensure accuracy and reliability for analysis.
Communicate Analytical Findings: Presents complex data insights and technical information to stakeholders in a clear and understandable manner, facilitating informed decision-making.
Standardize Analytical Processes: Supports the development of consistent, repeatable workflows for data preparation, model development, and reporting to improve quality and reduce duplication of effort across the team.
Scale Data Solutions: Assists in transitioning analytical work from one-off analyses to scalable, reusable solutions that can be applied across multiple business areas or sites.
Support Model Deployment: Assists in moving developed models from experimentation into operational use, ensuring outputs are accessible and actionable for business stakeholders.
Document & Maintain Work Products: Maintains clear documentation of models, data processes, and analytical approaches to support knowledge sharing, continuity, and ongoing improvement.
Develop Business-Facing Tools & Applications: Builds lightweight applications and interactive tools that make model outputs and analytical capabilities accessible and usable by non-technical business users.
Translate Requirements into Working Solutions: Works with business stakeholders to understand their needs and converts those requirements into functional, user-ready data products.
Support Tool Maintenance & Iteration: Maintains and improves existing tools and applications based on user feedback and changing business needs, ensuring solutions remain accurate and relevant over time.
Data Analysis: Proficient in analyzing large and complex datasets to identify trends and derive actionable insights.
Statistical Modeling: Expertise in developing and applying statistical models to support business decision-making processes.
Machine Learning: Skilled in designing, implementing, and evaluating machine learning algorithms for predictive analytics.
Programming: Advanced proficiency in programming languages such as Python and R for data manipulation and analysis.
Data Visualization: Ability to create clear and informative visualizations using tools like Tableau, Power BI, or matplotlib.
Communication: Effectively conveys complex data findings to non-technical stakeholders in a clear and understandable manner.
Business Acumen: Understands key business drivers and aligns data science projects with organizational goals and strategies.
Data Management: Knowledgeable in data warehousing, data cleaning, and database management to ensure data integrity and accessibility.
Process Standardization: Ability to develop and follow consistent, repeatable approaches to data preparation, analysis, and model development that improve team efficiency and output quality.
Scalability Awareness: Understanding of how to design analytical solutions that can grow with the business, moving beyond one-time analyses toward reusable, maintainable work products.
Cross-Functional Integration & Partnership: Ability to connect data science outputs to the systems and workflows used by other teams, working effectively with IT, operations, and business stakeholders to ensure insights translate into day-to-day operational action.
Documentation & Knowledge Sharing: Skilled in clearly documenting processes, models, and methodologies to support team continuity and organizational learning.
Application Development: Ability to build functional, user-facing tools and applications that package analytical or ML capabilities for business use.
User-Centered Thinking: Designs solutions with the end user in mind, ensuring tools are intuitive, practical, and solve real business problems.
Iterative Development: Comfortable building, testing, and refining solutions based on stakeholder feedback rather than waiting for a perfect first release.
Advancement opportunities in international team
A total rewards package, that includes: private health insurance, life & accidental insurance
Multisport package
Meal Vouchers
Company performance bonus program
Work‑life balance, semi‑flexible working time
PPK: company offers payment to your PPK account up to 4% of your compensation depending on seniority
Hybrid work model
Relocation & Educational support